{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UCI数据---- Adults"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Set Information:\n",
    "\n",
    "Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0)) \n",
    "\n",
    "Prediction task is to determine whether a person makes over 50K a year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn import cross_validation as cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os,time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "FileExistsError",
     "evalue": "[WinError 183] 当文件已存在时，无法创建该文件。: 'feature'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-28-8aa2f4681b8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"feature\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mFileExistsError\u001b[0m: [WinError 183] 当文件已存在时，无法创建该文件。: 'feature'"
     ]
    }
   ],
   "source": [
    "os.mkdir(\"feature\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "adult_data = \"adult_data.txt\"\n",
    "data_train_gender = np.loadtxt(adult_data, dtype=str, delimiter=\",\", usecols=(9,14))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "准备训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names = [\"age\", \"workclass\", \"fnlwgt\", \"education\", \"education_num\", \"marital_status\", \"occupation\", \"relationship\", \n",
    "          \"race\", \"sex\", \"capital_gain\", \"capital_loss\", \"hours_per_week\", \"native_country\", \"income\"]\n",
    "train_data = pd.read_csv(\"adult_data.txt\", names=names)\n",
    "\n",
    "#train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  fnlwgt   education  education_num  marital_status  \\\n",
       "0   39          4   77516   Bachelors             13               2   \n",
       "1   50          2   83311   Bachelors             13               1   \n",
       "2   38          1  215646     HS-grad              9               3   \n",
       "3   53          1  234721        11th              7               1   \n",
       "4   28          1  338409   Bachelors             13               1   \n",
       "\n",
       "           occupation  relationship  race      sex  capital_gain  \\\n",
       "0        Adm-clerical             2     1     Male          2174   \n",
       "1     Exec-managerial             1     1     Male             0   \n",
       "2   Handlers-cleaners             2     1     Male             0   \n",
       "3   Handlers-cleaners             1     2     Male             0   \n",
       "4      Prof-specialty             5     2   Female             0   \n",
       "\n",
       "   capital_loss  hours_per_week  native_country  income  \n",
       "0             0              40   United-States   <=50K  \n",
       "1             0              13   United-States   <=50K  \n",
       "2             0              40   United-States   <=50K  \n",
       "3             0              40   United-States   <=50K  \n",
       "4             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data['workclass'] = train_data.workclass.str.replace('?', 'Other')\n",
    "\n",
    "train_data['workclass'] = Series(map(workclass_trans, train_data.workclass.tolist()))\n",
    "\n",
    "# marital_status :   str --> int\n",
    "train_data['marital_status'] = Series(map(marital_trans, train_data.marital_status.tolist()))\n",
    "\n",
    "# relation_ :   str --> int\n",
    "train_data['relationship'] = Series(map(relation_trans, train_data.relationship.tolist()))\n",
    "\n",
    "# race :   str --> int\n",
    "train_data['race'] = Series(map(race_trans, train_data.race.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def race_trans(obj):\n",
    "    dic = {' White': 1,\n",
    "           ' Black': 2,\n",
    "           ' Asian-Pac-Islander': 3,\n",
    "           ' Amer-Indian-Eskimo': 4,\n",
    "           ' Other': 5}\n",
    "    return dic[obj]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def workclass_trans(obj):\n",
    "    dic = {' Private':1,\n",
    "           ' Self-emp-not-inc':2,\n",
    "           ' Local-gov':3,\n",
    "           ' State-gov':4,\n",
    "           ' Self-emp-inc':5,\n",
    "           ' Federal-gov':6,\n",
    "           ' Without-pay':7,\n",
    "           ' Never-worked':8,\n",
    "           ' Other':9}\n",
    "    return dic[obj]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def marital_trans(obj):\n",
    "    dic = {' Married-civ-spouse':1,\n",
    "           ' Never-married':2, \n",
    "           ' Divorced': 3, \n",
    "           ' Separated': 4,\n",
    "           ' Widowed': 5,\n",
    "           ' Married-spouse-absent': 6,\n",
    "           ' Married-AF-spouse': 7}\n",
    "    return dic[obj]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def relation_trans(obj):\n",
    "    dic = {' Husband': 1,\n",
    "           ' Not-in-family': 2,\n",
    "           ' Own-child': 3,\n",
    "           ' Unmarried': 4,\n",
    "           ' Wife': 5,\n",
    "           ' Other-relative': 6}\n",
    "    return dic[obj]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "准备测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 读取测试数据\n",
    "names = [\"age\", \"workclass\", \"fnlwgt\", \"education\", \"education_num\", \"marital_status\", \"occupation\", \"relationship\", \n",
    "          \"race\", \"sex\", \"capital_gain\", \"capital_loss\", \"hours_per_week\", \"native_country\", \"income\"]\n",
    "test_data = pd.read_csv(\"adult_test.txt\", names=names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  fnlwgt    education  education_num  marital_status  \\\n",
       "0   25          1  226802         11th              7               2   \n",
       "1   38          1   89814      HS-grad              9               1   \n",
       "2   28          3  336951   Assoc-acdm             12               1   \n",
       "\n",
       "           occupation  relationship  race    sex  capital_gain  capital_loss  \\\n",
       "0   Machine-op-inspct             3     2   Male             0             0   \n",
       "1     Farming-fishing             1     1   Male             0             0   \n",
       "2     Protective-serv             1     1   Male             0             0   \n",
       "\n",
       "   hours_per_week  native_country  income  \n",
       "0              40   United-States   <=50K  \n",
       "1              50   United-States   <=50K  \n",
       "2              40   United-States    >50K  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16281, 15)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  测试数据处理\n",
    "test_data['workclass'] = test_data.workclass.str.replace('?', 'Other')\n",
    "\n",
    "test_data['workclass'] = Series(map(workclass_trans, test_data.workclass.tolist()))\n",
    "\n",
    "# marital_status :   str --> int\n",
    "test_data['marital_status'] = Series(map(marital_trans, test_data.marital_status.tolist()))\n",
    "\n",
    "# relation_ :   str --> int\n",
    "test_data['relationship'] = Series(map(relation_trans, test_data.relationship.tolist()))\n",
    "\n",
    "# race :   str --> int\n",
    "test_data['race'] = Series(map(race_trans, test_data.race.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education_num</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32561.000000</td>\n",
       "      <td>3.256100e+04</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.581647</td>\n",
       "      <td>1.897784e+05</td>\n",
       "      <td>10.080679</td>\n",
       "      <td>1077.648844</td>\n",
       "      <td>87.303830</td>\n",
       "      <td>40.437456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.640433</td>\n",
       "      <td>1.055500e+05</td>\n",
       "      <td>2.572720</td>\n",
       "      <td>7385.292085</td>\n",
       "      <td>402.960219</td>\n",
       "      <td>12.347429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.178270e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.783560e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.370510e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  education_num  capital_gain  capital_loss  \\\n",
       "count  32561.000000  3.256100e+04   32561.000000  32561.000000  32561.000000   \n",
       "mean      38.581647  1.897784e+05      10.080679   1077.648844     87.303830   \n",
       "std       13.640433  1.055500e+05       2.572720   7385.292085    402.960219   \n",
       "min       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.178270e+05       9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.783560e+05      10.000000      0.000000      0.000000   \n",
       "75%       48.000000  2.370510e+05      12.000000      0.000000      0.000000   \n",
       "max       90.000000  1.484705e+06      16.000000  99999.000000   4356.000000   \n",
       "\n",
       "       hours_per_week  \n",
       "count    32561.000000  \n",
       "mean        40.437456  \n",
       "std         12.347429  \n",
       "min          1.000000  \n",
       "25%         40.000000  \n",
       "50%         40.000000  \n",
       "75%         45.000000  \n",
       "max         99.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#train_data.age.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAGbCAYAAAAFjNDAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzt3XucHWWV7//PVxAyAblohOhovDEnE+8EB4gXFBiCIG6P\nekZEUQneJ2Q48QhezjgJqL8xcQSPSXS8xAsXOyj+zDCiEFFRowhn0uqgJHgZsBFMsCVcJAaQrPNH\n1SY71d1JZ/fufp6q/r5fr3qld9Wz9157JXtlddVTVYoIzMzMzHL1sNQBmJmZme2MmxUzMzPLmpsV\nMzMzy5qbFTMzM8uamxUzMzPLmpsVMzMzy5qbFTMzM8uamxUzMzPLmpsVMzMzy5qbFTMzm7QknSZp\nm6QZqWOxkblZMTOzySzKxTIm3xvIzMwmK0kCHh4R96eOxUbmZsXMzMyy5sNAtkuS9pX0UUk3Sdoq\naZOkNZKe3THmCElXSLpT0r2Srpb03I7tUyStL5e9O9YfKOl3ktaWv+GYmU2Y6pwVSTdLukzS8yRd\nK+lPkn4t6XXDPHd/Sed31MZbJH1B0iM7xjxa0kpJG8vX+omk11de5wllDO+Q9Pfl+90r6UpJf1mO\neV/5+lskrZZ0wDDxnCDpe5L+KOluSV+T9NTeZ23iuVmx0fgk8Fbgy8DbgQ8DW4BZAJKOAb4L7Ass\nBt4D7A98W9JzACJiK/AG4BDggx2v/XHgEcAbwrv5zGziVeesBPBXFPVuDfAO4A7gc5JmtQdJ2gdY\nC8wHrgD+AfgEMBN4XDlmCkVtfC1wIfBO4E7g85IWDBPLqRQ19mPAvwAvBL4s6QPAXOBDFPX4peX2\nh5TN1NeAe4CzgXMpavT3GzF5OCK8eNnpAmwGPraT7TcCl1fW7Q38Griisv6DwAPA84D/AWwDzkj9\nGb148TI5F4pfoh4EZpSPbyofP7djzDTgT8DSjnXnlONaO3ntM8sxr+5YtwfwA+AuYJ9y3RPKWrgR\n2Ldj7AfL9f3AwzrWX1zG8/Dy8T4UDdUnKu//6LJ+/2vqPI918Z4VG407gSMkPaa6oTwU9FdAn6RH\ntReKvSXfAo6qPGUx8HPgAmAF8J2IWD6ewZuZ7aYbIuKH7QcRMUjxS9mTO8a8AvhpRFy2k9c5AdgY\nEas6XutBij0n+1LsOen0pYj4Y8fja8s/L4yIbZX1ewF/WT6eS7E3e1WlDkc59uidftoa2DN1AFYL\nZwOfB26RtA74OnBBRNxE0ahA0XwMZ5uk/SPiLoCIeEDSG4H/S/GbwenjGrmZ2e4bGGbdZuDAjsdP\nAS7dxes8AfjlMOvXAyq3d7ql8viu8s/fjrD+QOBmisPrAr4zzHtFx/jacrNiuxQRX5b0PeDlFB38\nO4F3SXo52+c9/S/gpyO8xB8rj19c/jmFotn5TW8jNjMbkwdHWD/eJwGM9L67iudhFE3JqcCmYcb9\neYxxJedmxUYlIjYB/wr8q6RpwI+B/00x+Qzgnoj49q5eR9IzgfcBnwWeDXxG0jMi4p7xidzMbFz8\nGnj6Lsb8BnjGMOtndWzvVSwCfj+aOlxHnrNiOyXpYZL261xXHr+9Ddg7Iv6D4ovyznJ2fPX50zp+\n3pPicNJvKSaezQOmA+eP2wcwMxsfXwGeJellOxnzdWC6pJPbKyTtASygOGvnuz2K5UrgbuC9ZZ3d\nQWcdrivvWbFdeQTwW0mXUhzm+SNwHPActu9VeRPFl/Lnkj4H3Eox8etoimOl7S/z+4BnAsdExL3A\n9ZLOBT4g6SsR8Y0J+kxmZmP1YYozGr9c1r11wKMoTit+a0RcD3yK4rIPny8v43Az8HfAHODMsg52\n66FDUhFxj6S3U8wd7Je0Cvg9MAN4CcUp1v8whvdKzs2K7coWirN25lLMWXkY8Cvg7RHxKYCI+K6k\nORTNyHyKWe4bKWahfxJA0qHAu4FlEfG9jtf/EEUz8ylJT4uIuyfkU5mZDW9n9wp6aH1E3Cvp+RSn\nML8ceD1wO3AV5YTYiNgq6YUUde71wH4UZxWdFhEXjvJ9dxlL+V59km6lqLPvpLh8xK3A94HPjfAa\nteHL7ZuZmVnWks9ZkfQeSdeVlwbeJOmrkv5bZcznyksRdy5fr4zZW9IKSYOS7pF0qaSDKmMOlHSx\npLskbZb0meo8C0mPl3R5eanjjZKWSkqeJzObfFwfzQo5/CN7AbAMOAL4W+DhwBpJf1EZ9w3gYIoJ\nmdOBUyrbP0pxbO6VFBcieyzFBKhOX6SYhX1sOfYoysMUUEwmpZh7sSdwJMWVDU+juGyxmdlEc300\nI8PDQOWs5duBoyJibbnuc8D+EfGKEZ6zH8VkoldHxFfLdTMpLrxzZERcV97T4efAYRHx43LM8cDl\nwOMiYqOkE4DLgMeUZ7wg6a0UxxsfHRG1P1fdzOrL9dEmqxz2rFQdQDFx6I7K+heVu0E3SPq4Ou5q\nCRxG0e1/q70iIm6kuArhnHLVkcDm9hexdFX5Xkd0jLm+/UUsXUlxGeOnje1jmZmNmeujTUpZnQ0k\nSRS7K9dGxA0dm75BscvyJopLHP8z8HVJc6LYNTQduH+YM0k2ldso/7y9c2NEPCjpjsqY6tX/NnVs\nG3KF1vL+C8dTnJK2dXSf1CaZKcATgSsj4g+JY7Gacn20hhpVfcyqWQE+DjyV4o68D4mIL3U8/Lmk\n6ykuRPYihr8XwkQ6nuIOmGa78lqKeQFm3XB9tCbbaX3MplmRtBw4EXhBRPxuZ2Mj4iZJgxQ3b/oO\nxTU99pK0X+W3h4PLbZR/Vme/7wE8sjLmbypvd3DHtuHcDHDRRRcxa9asEYZMjIULF3L++b4YbFsu\n+Vi/fj2nnnoqlP9WzHaX6+PY5VIPcpJDTkZbH7NoVsov4suAF0bEcHe7rI5/HMWVAttf2nUUN2o6\nFuicQDYDuKYccw1wgKRDO47LHktxFcBrO8a8V9K0juOycymuwtq527XTVoBZs2Yxe/bsUXza8bP/\n/vsnjyEnGebDu8Ftt7k+9kaG9SC5zHKy0/qYvFmR9HGK0+xawL2S2p36XeXV//YBFlEck91I8dvC\nEuAXFJO7iIi7Ja0EzpO0meKeCx8DfhAR15VjNki6Evi0issS70VxSmBfRLR/K1hD8aW7UNK7gMcA\n7weWR8QD45qIHti4caRfbiYn58PqzvWxMDAwwODg4K4H7sSvf/1r+vv7x/Qa06ZNY8aMGWN6jZzU\nqUYmb1aAt1HMOL+6sn4exX0OHqS4n8zrKWbC30bxJfynyhdkYTn2UorLDF9Bcen3Tq8BllPMct9W\njj2zvTEitkk6CfgE8EPgXoob7y0a20ecGLfeemvqELLifFgDTPr6ODAwwMyZs9i6dcuYX+uwww4b\n0/OnTJnKjTeub0zDUqcambxZiYidnj4dEVuBF4/ide6juJPlgp2MuRM4dRevcwtw0q7eL0dj/SI2\njfNhdef6CIODg2WjchHFNeu6tZCx3eB9PVu3nsrg4GBjmpU61cjkzYr1zimnVC9aObk5H2ZNMgsY\ny/yKt43x+c1TpxqZ40XhrEt1+oc3EZwPM9vO9aCqTjXSzYqZmZllzc1Kg8ybNy91CFlxPsxsO9eD\nqjrVSDcrDTJ37tzUIWTF+TCz7VwPqupUI92sNEidjj9OBOfDzLZzPaiqU410s2JmZmZZc7NiZmZm\nWXOz0iBr165NHUJWnA8z2871oKpONdLNSoMsXbo0dQhZcT7MbDvXg6o61Ug3Kw2yatWq1CFkxfkw\ns+1cD6rqVCPdrDTI1KlTU4eQFefDzLZzPaiqU410s2JmZmZZc7NiZmZmWXOz0iBnnXVW6hCy4nyY\n2XauB1V1qpFuVhpkxowZqUPIivNhZtu5HlTVqUa6WWmQBQsWpA4hK86HmW3nelBVpxrpZsXMzMyy\n5mbFzMzMsuZmpUE2bNiQOoSsOB9mtp3rQVWdaqSblQY5++yzU4eQFefDzLZzPaiqU410s9Igy5cv\nTx1CVpwPM9vO9aCqTjXSzUqD1Ok0tIngfJjZdq4HVXWqkW5WzMzMLGtuVszMzCxrblYaZMmSJalD\nyIrzYWbbuR5U1alGullpkC1btqQOISvOh5lt53pQVaca6WalQc4555zUIWTF+TCz7VwPqupUI92s\nmJmZWdbcrJiZmVnW3Kw0yODgYOoQsuJ8mNl2rgdVdaqRblYa5PTTT08dQlacDzPbzvWgqk410s1K\ngyxevDh1CFlxPsxsu8WpA8hOnWrknqkDsMLAwEBPdsn19/eP6fnTpk2r1SWYd2b27NmpQzCzbLge\nVNWpRrpZycDAwAAzZ85i69b057xPmTKVG29c35iGxczM6s/NSgYGBwfLRuUiYFbCSNazdeupDA4O\nulkxM7NsuFnJyizGtqtyJfDGHsVSfytXruSNb3Q+zAxcH4eqU430BNtGGdt8laYZ6/wdM2sS14Oq\nOtVINyuNsiJ1AFlZscL5MLM214OqOtVINytmZmaWNTcrZmZmljU3K2ZmZpY1NyuN0kodQFZaLefD\nzNpcD6rqVCPdrDTKGakDyMoZZzgfZtbmelBVpxrpZqVR5qYOICtz5zofZtbmelBVpxrpZsXMzMyy\n5mbFzMzMsuZmpVFWpw4gK6tXOx9m1uZ6UFWnGulmpVH6UgeQlb4+58PM2lwPqupUI92sNMolqQPI\nyiWXOB9m1uZ6UFWnGulmxczMzLKWvFmR9B5J10m6W9ImSV+V9N+GGXeupNskbZH0TUmHVLbvLWmF\npEFJ90i6VNJBlTEHSrpY0l2SNkv6jKR9KmMeL+lySfdK2ihpqaTkeTKzycf10ayQwz+yFwDLgCOA\nvwUeDqyR9BftAZLeRXFFn7cAhwP3AldK2qvjdT4KvAR4JXAU8FjgK5X3+iIwCzi2HHsU8MmO93kY\n8HVgT+BI4A3AacC5PfmkZma7x/XRDCAislqAacA24Pkd624DFnY83g/4E/Cqjsf3AS/vGDOzfJ3D\ny8ezyseHdow5HvgzML18fALwADCtY8xbgc3AniPEOxuIdevWRbfWrVsXQMC6gBjDctoYn1/EMZbP\nkpPTTjstdQgR0fn3y+zI4Dvmpb6L62O4PvZQDjVytPUxhz0rVQdQBH4HgKQnAdOBb7UHRMTdwLXA\nnHLVcyi6/c4xNwIDHWOOBDZHxI873uuq8r2O6BhzfUQMdoy5EtgfeFoPPts4q8/VCCdCna7OaDZK\nro9dcz2oqlONzKpZkSSK3ZVrI+KGcvV0ii/MpsrwTeU2gIOB+8sv6UhjpgO3d26MiAcpvvSdY4Z7\nHzrGZOyU1AFk5ZRTnA9rDtfHsXI9qKpTjcyqWQE+DjwVeHXqQHbXiSeeSKvV2mGZM2fOkIvurFmz\nZid3uqxeoKef4k6hg5X1i4AllXUD5dgNlfXLgLMq67aUY9dW1l8xbFQnn3zyqD/H/PnzWbly5Q7r\n+vv7abVaDA7u+DkWLVrEkiU7fo6BgQFarRYbNuz4OZYtW8ZZZ+34ObZs2UKr1WLt2h0/R19fH/Pm\nzUvyOfr6+mi1Whx33HFMnz6dY445hoULFw55fbMuuD7uwPXxoU8xGerjzo4RTeQCLAd+A8yorH8S\nxbHUZ1bWXw2cX/58NPAgsF9lzM3AmeXP84A/VLbvQXEM9mXl43OA/sqYJ5bv/6wR4s7omOxYl+Yd\nk82B56x4Gevi+uj62FS1mrMiaTnwMuDoiBjo3BYRNwEbKWaot8fvR3Ec9YflqnUUE8E6x8wEZgDX\nlKuuAQ6QdGjHyx8LiOL4bnvMMyRN6xgzF7gLuIHsVX8TmNyqv1GY1ZHrY6+4HlTVqUYmb1YkfRx4\nLfAa4F5JB5fLlI5hHwX+UdJLJT0DuAD4LfBv8NCEspXAeZJeJOkw4LPADyLiunLMBorJYJ+W9DeS\nnkexD7AvIjaW77OG4kt3oaRnSjoeeD+wPCIeGNdE9MTS1AFkZelS58PqzfWxl1wPqupUI/dMHQDw\nNopdQFdX1s+j+NIREUslTaU45/8A4PvACRFxf8f4hRS7Oi8F9qY4wDi/8pqvodidehXFrstLgTPb\nGyNim6STgE9Q/FZyL/B5ioOgNbAqdQBZWbXK+bDac33sGdeDqjrVyOTNSkSMau9ORCwGFu9k+33A\ngnIZacydwKm7eJ9bgJNGE1N+pqYOICtTpzofVm+uj73kelBVpxqZ/DCQmZmZ2c64WTEzM7OsuVlp\nlOr1Aia36nUHzGwycz2oqlONdLPSKDNSB5CVGTOcDzNrcz2oqlONdLPSKCPOnZuUFixwPsyszfWg\nqk410s2KmZmZZc3NipmZmWXNzUqjVG/SNblVb/ZlZpOZ60FVnWqkm5VGOTt1AFk5+2znw8zaXA+q\n6lQj3aw0yvLUAWRl+XLnw8zaXA+q6lQj3aw0Sn1OQ5sIdTotz8zGm+tBVZ1qpJsVMzMzy5qbFTMz\nM8uam5VGWZI6gKwsWeJ8mFmb60FVnWqkm5VG2ZI6gKxs2eJ8mFmb60FVnWqkm5VGOSd1AFk55xzn\nw8zaXA+q6lQj3ayYmZlZ1tysmJmZWdbcrDTKYOoAsjI46HyYWZvrQVWdaqSblUY5PXUAWTn9dOfD\nzNpcD6rqVCPdrDTK4tQBZGXx4sWpQzCzbCxOHUB26lQj3aw0yuzUAWRl9mznw8zaXA+q6lQj3ayY\nmZlZ1tysmJmZWdbcrDTKytQBZGXlSufDzNpcD6rqVCPdrDRKf+oAstLf73yYWZvrQVWdaqSblUZZ\nkTqArKxY4XyYWZvrQVWdaqSbFTMzM8uamxUzMzPLmpsVMzMzy5qblUZppQ4gK62W82Fmba4HVXWq\nkW5WGuWM1AFk5YwznA8za3M9qKpTjXSz0ihzUweQlblznQ8za3M9qKpTjXSzYmZmZllzs2JmZmZZ\nc7PSKKtTB5CV1audDzNrcz2oqlONdLPSKH2pA8hKX5/zYWZtrgdVdaqRblYa5ZLUAWTlkkucDzNr\ncz2oqlONdLNiZmZmWXOzYmZmZllzs2JmZmZZc7PSKPNSB5CVefOcDzNrcz2oqlONdLPSKPW5GuFE\nqNPVGc1svLkeVNWpRrpZaZRTUgeQlVNOcT7MrM31oKpONdLNipmZmWXNzYqZmZllzc1Ko6xNHUBW\n1q51PsyszfWgqk410s1KoyxNHUBWli51PsyszfWgqk410s1Ko6xKHUBWVq1yPsyszfWgqk410s1K\no0xNHUBWpk51PsyszfWgqk410s2KmZmZZS2LZkXSCyRdJulWSdsktSrbP1eu71y+Xhmzt6QVkgYl\n3SPpUkkHVcYcKOliSXdJ2izpM5L2qYx5vKTLJd0raaOkpZKyyJOZTT6uj2aZNCvAPsBPgL8HYoQx\n3wAOBqaXS/VqNh8FXgK8EjgKeCzwlcqYLwKzgGPLsUcBn2xvLL90Xwf2BI4E3gCcBpzb1aeacGel\nDiArZ53lfFgjuD72hOtBVZ1q5J6pAwCIiCuAKwAkaYRh90XE74fbIGk/4HTg1RHx3XLdPGC9pMMj\n4jpJs4DjgcMi4sflmAXA5ZLeGREby+1/DRwdEYPA9ZLeB3xI0uKI+HPPPvS4mJE6gKzMmOF8WP25\nPvaK60FVnWpkLntWRuNFkjZJ2iDp45Ie2bHtMIrG61vtFRFxIzAAzClXHQlsbn8RS1dR/KZyRMeY\n68svYtuVwP7A03r6acbFgtQBZGXBAufDJg3Xx11yPaiqU42sS7PyDeD1wDHA2cALga93/JYxHbg/\nIu6uPG9Tua095vbOjRHxIHBHZcymYV6DjjFmZjlxfbTGq0WzEhFfioivRcTPI+Iy4CTgcOBFaSPb\n7sQTT6TVau2wzJkzh9WrV+8wbs2aNbRarRFeZXXlcT/QAgYr6xcBSyrrBsqxGyrrlzH0WO2Wcmz1\n6oVXDBvVySefPOrPMX/+fFauXLnDuv7+flqtFoODO36ORYsWsWTJjp9jYGCAVqvFhg07fo5ly5YN\nOb66ZcsWWq3WkKsw9vX1DXvr84n4HH19fbRaLY477jimT5/OMcccw8KFC4e8vlmvuD66Pk6K+hgR\nWS3ANqA1inG3A28ufz4aeBDYrzLmZuDM8ud5wB8q2/cAHgBeVj4+B+ivjHliGdOzRohjNhDr1q2L\nbq1bty6AgHUBMYZl/RifX8Qxls+Sk/Xr16cOISI6/36ZHRl8x7zUd3F9DNfHHsqhRo62PtZiz0qV\npMcBjwJ+V65aB/yZYhZ7e8xMihlV15SrrgEOkHRox0sdCwi4tmPMMyRN6xgzF7gLuKHHH2McnJ06\ngKycfbbzYZOP6+NIXA+q6lQjszgbqDyX/xCKLwbAkyU9i+J46R0U+/W+Amwsxy0BfkExuYuIuFvS\nSuA8SZuBe4CPAT+IiOvKMRskXQl8WtLbgb0o9gH2RTHTHWANxZfuQknvAh4DvB9YHhEPjGcOemN5\n6gCysny582H15/rYK64HVXWqkVk0K8BzgO9Q7AoK4CPl+i9QXFvgmRQTyA4AbqP4Ev5T5QuykGJX\n56XA3hQHGOdX3uc1FP9ir6LYdXkpcGZ7Y0Rsk3QS8Angh8C9wOcpikEN1Oc0tIlQp9PyzHbC9bEn\nXA+q6lQju2pWJD0eiIj4bfn4cIp/6DdExKd29/WiOPd/Z4ekXjyK17iP4ty0Ec/Fiog7gVN38Tq3\nUExQMzPbba6PZr3X7ZyVL1JM2kLSdOCbFLPPPyjpn3oUm5lZHbk+mvVYt83K04Hryp9fBfwsIp4L\nvJbi8suWRPV0vcmtetqf2QRxfcyS60FVnWpkt83Kw4H7yp//Fris/HkDxaQrS2JL6gCysmWL82FJ\nuD5myfWgqk41sttm5efA2yS9ADiO7VfLeSzwh14EZt04J3UAWTnnHOfDknB9zJLrQVWdamS3zcq7\ngLcCV1Oc2vbTcn2L7bs/zcwmI9dHsx7b7bOByvtN/BfFeWB7RsTmjs2fwvvazGyScn00Gx/d7FkR\n8CtgeuWLSETcHBG3D/80G3/Ve2RMbtV7VJhNANfHbLkeVNWpRu52sxIR24BfUlzO2bJyeuoAsnL6\n6c6HTSzXx5y5HlTVqUZ2O2fl3cCHJT29l8HYWC1OHUBWFi9enDoEm5xcH7O0OHUA2alTjez2cvsX\nAFOBn0q6H/hT58aIeORYA7NuzE4dQFZmz3Y+LAnXxyy5HlTVqUZ226z8z55GYWbWHK6PZj3WVbMS\nEV/odSBmZk3g+mjWe93OWUHSUyR9QFKfpIPKdSdIelrvwrPdszJ1AFlZudL5sDRcH3PkelBVpxrZ\nVbMi6YXA9cARwCuAfctNz8KXCUyoP3UAWenvdz5s4rk+5sr1oKpONbLbPSsfAv4xIo4D7u9Y/23g\nyDFHZV1akTqArKxY4XxYEq6PWXI9qKpTjey2WXkG8NVh1t8OTOs+HDOz2nN9NOuxbpuVOxn+7qGH\nArd2H46ZWe25Ppr1WLfNyipgiaTpQAAPk/Q84F8orjFgZjZZuT6a9Vi3zcp7gQ3ALRSTx24Avgf8\nEPhAb0Kz3ddKHUBWWi3nw5JwfcyS60FVnWpkt9dZuR94s6T3A0+n+EL+OCJ+2cvgbHedkTqArJxx\nhvNhE8/1MVeuB1V1qpHdXsEWgIgYkHRL+XP0JiTr3tzUAWRl7lznw9JxfcyN60FVnWrkWC4K90ZJ\nPwO2Alsl/UzSm3oXmplZPbk+mvVWV3tWJJ0LvANYBlxTrp4DnC9pRkT8U4/iMzOrFddHs97rds/K\n24E3R8R7IuKycnkP8Bbg73sXnu2e1akDyMrq1c6HJeH6mCXXg6o61chum5WHA/8xzPp1jHEejI1F\nX+oAstLX53xYEq6PWXI9qKpTjey2WbmQ4reHqrcAF3cfjo3NJakDyMollzgfloTrY5ZcD6rqVCNH\n3eVLOq/jYQBvkjQX+FG57ghgBr7okZlNMq6PZuNrd3ZJHlp5vK788ynln4Pl4lugm9lk4/poNo5G\n3axExNHjGYiZWV25PpqNr66vs2I5mpc6gKzMm+d8mFmb60FVnWpkt9dZmQIsAI4GDqLS9ETE7LGH\nZruvPlcjnAh1ujqjNYfrY65cD6rqVCO7PY1uJcXf/KXAdRQTyiy5U1IHkJVTTnE+LAnXxyy5HlTV\nqUZ226ycBJwYET/oZTBmZg3g+mjWY93OWbkVuKeXgZiZNYTro1mPddus/C9giaQn9DIYG6u1qQPI\nytq1zocl4fqYJdeDqjrVyG6blf8ApgD/JekeSXd0Lj2Mz3bL0tQBZGXpUufDknB9zJLrQVWdamS3\nc1b6gL8E3gtswhPIMrEqdQBZWbXK+bAkXB+z5HpQVaca2W2z8lxgTkT8tJfB2FhNTR1AVqZOdT4s\nCdfHLLkeVNWpRnZ7GGgD8Be9DMTMrCFcH816rNtm5d3ARyS9SNKjJO3XufQyQDOzmnF9NOuxbpuV\nK4A5wLeA24HN5XJn+aclcVbqALJy1lnOhyXh+pgl14OqOtXIbues+KZdWZqROoCszJjhfFgSro9Z\ncj2oqlON7KpZiYjv9joQ64UFqQPIyoIFzodNPNfHXLkeVNWpRnZ7I8OjdrY9Ir7XXThmZvXm+mjW\ne90eBrp6mHWd1xLYo8vXNTOru6uHWef6aDYG3U6wPbCyHAS8GPi/+D7cCW1IHUBWNmxwPiwJ18cs\nuR5U1alGdjtn5a5hVn9T0v3AecBhY4rKunQ2cFnqIHpiYGCAwcHBMb3GwoULOf/888ccy7Rp02o1\nEc3Scn3MVXPqY6+cffbZXHZZPXLS7WGgkWwCZvb4NW3UlqcOoCcGBgaYOXMWW7duGfNrHXbY2P9f\nmDJlKjfeuN4Ni42V62NSzaiPvbR8eX1y0u0E22dWVwGPobgY0k/GGpR1qxn/mQ4ODpaNykXArMTR\nrGfr1lMZHBx0s2Kj4vqYK39/q+pU07rds/ITigljqqz/EXD6mCIye8gsYHbqIMx2l+ujWY9126w8\nqfJ4G/D7iNg6xnjMzOrO9dGsx7o6GygifgMcArwVeB9wDvBxSZ+V9NndfT1JL5B0maRbJW2T1Bpm\nzLmSbpO0RdI3JR1S2b63pBWSBiXdI+lSSQdVxhwo6WJJd0naLOkzkvapjHm8pMsl3Stpo6Slkro9\na2qCLUlv+uHDAAAZeUlEQVQdQGacD5t4ro+5cj2oWrKkPjnp6h+ZpEXAGuBYYBpDT9XbXftQ7Dr9\ne3a8HkH7/d4FnAG8BTgcuBe4UtJeHcM+CrwEeCVwFPBY4CuVl/oixbGFY8uxRwGf7HifhwFfp9jj\ndCTwBuA04NwuPlMCY5+Q2izOh00818dcuR5UbdlSo5xExG4vwO+A13Xz3FG89jagVVl3G7Cw4/F+\nwJ+AV3U8vg94eceYmeVrHV4+nlU+PrRjzPHAn4Hp5eMTgAeAaR1j3kpx87E9R4h3NhDr1q2Lbq1b\nty6AgHUBkXAp4hjLZ+mFfPLRm5xs/zzMjnH4znjJa3F93CE210fbqdHWx2533+0F/LDL5+4WSU8C\nplPcwRSAiLgbuJbizqYAz6Ho9jvH3AgMdIw5EtgcET/uePmrKJJ0RMeY6yOi8wIfVwL7A0/r0Ucy\ns2ZzfTTrsW6blc8Ar+llIDsxneILs6myflO5DeBg4P7ySzrSmOkUt2t/SEQ8CNxRGTPc+9Axxsxs\nZ1wfzXqs22ZlCvAOSd+VtEzSeZ1LLwOsixNPPJFWq7XDMmfOHFavXr3DuDVr1tBqDZkfV1pdedwP\ntIDqlVwXMXSy2ADFFb2rl09eBpxVWbelfN21lfVXDBvVySefPOrPMX/+fFauXLnDuv7+flqt1pAr\n0i5atGjIBK+BgQEWLlw4TBS78zn6gHkMzdvJDM3xmvI1hnwSYPc/R19fH61Wi+OOO47p06dzzDHH\njPB5rMFcHytcHwu9qI+tVmvIZfKXLVvGWWft+Dm2bNlCq9Vi7dodP0dfXx/z5s0D2OE9J+JzjKk+\n7uwY0UgL8J2dLN/u5jU7XnuHY7IUpwFuA55ZGXc1cH7589HAg8B+lTE3A2eWP88D/lDZvgfFMdiX\nlY/PAforY55Yvv+zRog3o2OyL23EMdl88tGbnHjOyuRaXB932O76mLGXvvSlqUMYdX3s9t5AR3fz\nvC7f6yZJGylmqP8ngKT9KI6jriiHraOYCHYs8NVyzEyKSxZeU465BjhA0qGx/bjssRQXbrq2Y8x7\nJU2L7cdl5wJ3ATeMzyfspcWpA8jM4tQB2CTk+pirxakDyM7ixYtThzBqvb43UFfKc/kPYfsVH58s\n6VnAHRFxC8Vpd/8o6VcUvw28H/gt8G9QTCiTtBI4T9Jm4B7gY8APIuK6cswGSVcCn5b0dopJcMuA\nvojYWL7vGoov3YXl6YCPKd9reUQ8MK5J6Alf7XVHzofVn+tjr7geVM2eXZ+cZNGsUMxW/w7FrqAA\nPlKu/wJwekQslTSV4pz/A4DvAydExP0dr7GQYlfnpcDeFAcY51fe5zUUd7O6imLX5aXAme2NEbFN\n0knAJyhm898LfJ7iIKiZWQqujzbpZdGsRMR32cVk34hYzE7240XEfcCCchlpzJ3Aqbt4n1uAk3Y2\nxsxsorg+mnV/NpBlaeWuh0wqzoeZtbkeVFXP6MmZm5VG6U8dQGacDzNrcz2o6u+vT07crDTKil0P\nmVScDzNrcz2oWrGiPjlxs2JmZmZZc7NiZmZmWXOzYmZmZllzs9IoI91TY7JyPsyszfWgauT7MOXH\nzUqjnJE6gMw4H2bW5npQdcYZ9cmJm5VGmZs6gMw4H2bW5npQNXdufXLiZsXMzMyy5mbFzMzMsuZm\npVFWpw4gM86HmbW5HlStXl2fnLhZaZS+1AFkxvkwszbXg6q+vvrkxM1Ko1ySOoDMOB9m1uZ6UHXJ\nJfXJiZsVMzMzy5qbFTMzM8uamxUzMzPLmpuVRpmXOoDMOB9m1uZ6UDVvXn1y4malUepzNcKJ4XyY\nWZvrQZWvYGuJnJI6gMw4H2bW5npQdcop9cnJnqkDMDMzs90zMDDA4OBg6jCYNm0aM2bMGPf3cbNi\nZmZWIwMDA8ycOYutW7ekDoUpU6Zy443rx71hcbPSKGuB56cOIiPOh5m1NaceDA4Olo3KRcCsMbzS\nj4FDx/D89WzdeiqDg4NuVmx3LKUpX8becD7MrK2J9WAWMHsMz18MvLE3oYwzT7BtlFWpA8iM82Fm\nba4HQ9UnJ25WGmVq6gAy43yYWZvrwVD1yYmbFTMzM8uamxUzMzPLmpuVRjkrdQCZcT7MrM31YKj6\n5MTNSqOM/4V56sX5MLM214Oh6pMTNyuNsiB1AJlxPsyszfVgqPrkxM2KmZmZZc3NipmZmWXNzUqj\nbEgdQGacDzNrcz0Yqj45cbPSKGenDiAzzoeZtbkeDFWfnLhZaZTlqQPIjPNhZm2uB0PVJyduVhql\nPqehTQznw8zaXA+Gqk9O3KyYmZlZ1tysmJmZWdbcrDTKktQBZMb5MLM214Oh6pMTNyuNsiV1AJlx\nPsyszfVgqPrkxM1Ko5yTOoDMOB9m1uZ6MFR9cuJmxczMzLLmZsXMzMyy5malUQZTB5AZ58PM2lwP\nhqpPTtysNMrpqQPIjPNhZm2uB0PVJyduVhplceoAMrM4dQBmlo3FqQPI0OLUAYyam5VGmZ06gMw4\nH2bW5nowVH1y4mbFzMzMsuZmxczMzLLmZqVRVqYOIDPOh5m1uR4MVZ+c1KJZkbRI0rbKckNlzLmS\nbpO0RdI3JR1S2b63pBWSBiXdI+lSSQdVxhwo6WJJd0naLOkzkvaZiM/YG/2pA8iM82HN5/o4Wq4H\nQ9UnJ7VoVko/Aw4GppfL89sbJL0LOAN4C3A4cC9wpaS9Op7/UeAlwCuBo4DHAl+pvMcXgVnAseXY\no4BPjsNnGScrUgeQGefDJg3Xx11yPRiqPjnZM3UAu+HPEfH7EbadCbw/Ir4GIOn1wCbgvwNfkrQf\nxQnlr46I75Zj5gHrJR0eEddJmgUcDxwWET8uxywALpf0zojYOK6fzsyse66P1mh12rPyV5JulfRr\nSRdJejyApCdR/CbxrfbAiLgbuBaYU656DkVj1jnmRmCgY8yRwOb2F7F0FRDAEePzkczMesL10Rqt\nLs3Kj4DTKDr7twFPAr5XHi+dTvGF2VR5zqZyGxS7R+8vv6QjjZkO3N65MSIeBO7oGGNmlhvXR2u8\nWjQrEXFlRHwlIn4WEd8ETgQOBF6VOLSHnHjiibRarR2WOXPmsHr16h3GrVmzhlarNcKrrK487gda\nDL1/wyJgSWXdAEXN2FBZvww4q7JuS/m6ayvrrxg2qpNPPnnUn2P+/PmsXLnjDPP+/n5arRaDgzt+\njkWLFrFkyY6fY2BggIULFw4Txe58jj5gXrlth0/C0ByvGWYcwHyqM+VH8zn6+vpotVocd9xxTJ8+\nnWOOOWaEz2PWG66Pk6s+tlotbrrppjF8jnZ9hB1rX+b1MSJquQDXAR+k+C1iG/DMyvargfPLn48G\nHgT2q4y5GTiz/Hke8IfK9j2AB4CX7SSO2UCsW7cuurVu3boAAtYFxBiWK8f4/CKOsXyWXsgnH73J\nyfbPw+zI4LvjpfmL6+NwSzPqY9NyMtr6WIs9K1WS9gUOAW6LiJuAjRQz1Nvb96M4jvrDctU64M+V\nMTOBGcA15aprgAMkHdrxVscCoji+WwNzUweQGefDJh/Xx5G4HgxVn5zU4mwgSR8G/h34DfCXwDkU\nHf2qcshHgX+U9CuK3wbeD/wW+DeAiLhb0krgPEmbgXuAjwE/iIjryjEbJF0JfFrS24G9KPat9YVn\nuptZplwfbTKoRbMCPI7iHP9HAb+nOAh3ZET8ASAilkqaSnHO/wHA94ETIuL+jtdYSLGr81Jgb4oD\nkPMr7/MaYDnFLPdt5dgzx+kzmZn1guujNV4tmpWIOGUUYxazk/tdR8R9wIJyGWnMncCpux9hLlZT\nXDrBCs6HNZ/r42i5HgxVn5zUcs6KjaQvdQCZcT7MrM31YKj65MTNSqNckjqAzDgfZtbmejBUfXLi\nZsXMzMyy5mbFzMzMsuZmxczMzLLmZqVR5u16yKTifJhZm+vBUPXJiZuVRqnP1QgnhvNhZm2uB0PV\nJyduVhpll5dbmGScDzNrcz0Yqj45cbNiZmZmWXOzYmZmZllzs9Ioa1MHkBnnw8zaXA+Gqk9O3Kw0\nytLUAWTG+TCzNteDoeqTEzcrjbJq10MmFefDzNpcD4aqT07crDTK1NQBZMb5MLM214Oh6pMTNytm\nZmaWNTcrZmZmljU3K41yVuoAMuN8mFmb68FQ9cmJm5VGmZE6gMw4H2bW5nowVH1y4malURakDiAz\nzoeZtbkeDFWfnLhZMTMzs6y5WTEzM7OsuVlplA2pA8iM82Fmba4HQ9UnJ25WGuXs1AFkxvkwszbX\ng6HqkxM3K42yPHUAmXE+zKzN9WCo+uTEzUqj1Oc0tInhfJhZm+vBUPXJiZsVMzMzy5qbFTMzM8ua\nm5VGWZI6gMw4H2bW5nowVH1y4malUbakDiAzzoeZtbkeDFWfnLhZaZRzUgeQGefDzNpcD4aqT07c\nrJiZmVnW3KyYmZlZ1tysNMpg6gAy43yYWZvrwVD1yYmblUY5PXUAmXE+zKzN9WCo+uTEzUqjLE4d\nQGYWpw7AzLKxOHUAGVqcOoBRc7PSKLNTB5AZ58PM2lwPhqpPTtysmJmZWdbcrJiZmVnW3Kw0ysrU\nAWTG+TCzNteDoeqTEzcrjdKfOoDMOB9m1uZ6MFR9cuJmpVFWpA4gM86HmbW5HgxVn5y4WTEzM7Os\nuVkxMzOzrLlZMTMzs6y5WWmUVuoAMuN8mFmb68FQ9cmJm5VGOSN1AJlxPsyszfVgqPrkxM1Ko8xN\nHUBmnA8za3M9GKo+OXGzYmZmZllzs2JmZmZZc7PSKKtTB5AZ58PM2lwPhqpPTtysNMqS1AFkxvkw\nszbXg6HqkxM3K8OQNF/STZL+JOlHkv4mdUyj8+jUAWTG+TAbD/Wska4HQ9UnJ25WKiSdDHwEWAQc\nCvwUuFLStKSBmZllwDXSUnCzMtRC4JMRcUFEbADeBmwBTk8blplZFlwjbcK5Wekg6eHAYcC32usi\nIoCrgDmp4jIzy4FrpKWyZ+oAMjMN2APYVFm/CZg5wnOmAKxfv77rN93+3K8D3b8O/AC4eAzPv6kS\nTxr55AN6kZOO504ZYzBmqe1ujXR9HAdNyslo66OKptgAJD0GuBWYExHXdqxfAhwVEUN+c5D0Gsb+\nP6JNDq+NiC+mDsKsW7tbI10fbTfstD56z8qOBoEHgYMr6w8GNo7wnCuB1wI3A1vHLTKrsynAEyn+\nrZjV2e7WSNdH25VR1UfvWamQ9CPg2og4s3wsYAD4WER8OGlwZmaJuUZaCt6zMtR5wOclrQOuo5j5\nPhX4fMqgzMwy4RppE87NSkVEfKm8XsC5FLs2fwIcHxG/TxuZmVl6rpGWgg8DmZmZWdZ8nRUzMzPL\nmpsVMzNrBEmPGMWYF05ELLmQdNQoxiybiFjGws1KjUl6iqQPSOqTdFC57gRJT0sdWwrOh9mk9++S\n9h5pY9mofG0C48nBZZKePdLGslF5wwTG0xU3KzVVfumuB44AXgHsW256FnBOqrhScT7MDHgU8CVJ\nQ/5vK/cwXM7kO2vpM8AVkg6pbpD0f4B5wEsnPKrd5Galvj4E/GNEHAfc37H+28CRaUJKyvkws+OB\np1NpSCS9gGKPyhciYkGCuJKJiHdSXJf/KkmPba+X9FHgTcBLI+K7qeIbLTcr9fUM4KvDrL+d4v4d\nk43zYTbJRcRtwFzgb8u9Bkh6PsV/1l+MiPkp40voTUA/RcPyKEnnAW8BWhHxnbShjY6vs1JfdwKP\noX0nqe0Opbh3x2TjfJgZEfFrSS8Grpa0P/ByoC8i3pY4tGQiYpukV1McBlsP7EPRqHxr58/Mh5uV\n+loFLJH0d0AAD5P0POBfgAuSRpaG82E2yUnar/zxZop7En0VWA2c1bGNiLh74qNLQ9I/dDy8GngB\nxX14nirpqe0NEfGxCQ5tt/iicDUlaS9gBXAaxS3b/1z++UXgtIh4MF10E8/5MDNJ2yh+WXloVfln\ndDyOiNhjQgNLSFJ1b/NwIiKePO7BjIGblZqTNINiQtm+wI8j4peJQ0rK+TCbvEZ7DZU6TCi1HblZ\nMTMzs6x5zkqNlDO4RyUi3jGeseTA+TCz4UjaE3gaML1ctRG4ISIeSBdVOuVh8v8OzGHHnPwQ+LeI\nuH+k5+bCzUq9HDrKcZNld5nzYWYPKS8Gdy4wH9i/svkuScuBRRGxbcKDS6S8GNyVwGOBa4FN5aZD\ngbcBv5V0QkT8KlGIo+LDQGZm1giSllJMsn8fxX/Q7f+YD6a4/sr7gc9HxLuSBJiApG8C9wKvr54F\nVZ4hdQHwFxFxfIr4RsvNipmZNYKkjcAbIuLKEbYfD1wQEQdPbGTpSNoCHB4RPxth+zOAayNi6sRG\ntnt8GKjGJD0HeBUwA9irc1tEvCJJUAk5H2aT3iOA23ay/XcUF0SbTO4EnggM26yU2+6cqGC65cvt\n11R5NcIfArMortD4cIoJZccAdyUMLQnnw8woLnr2L5KG3GKjXLekHDOZfAa4QNJCSc+UdHC5PFPS\nQor7KH0qbYi75sNANSXpP4FPRsQKSfdQ3F34JuCTwO8iYlHSACeY82Fmkh5PcR+gv6a4C3vnnJVn\nADcAJ0XELWkiTEPSu4AzKc4E6rxA3kbgoxGxNFVso+VmpaYk3Qs8LSJulvQH4EURcb2kWcC3I+Ix\niUOcUM6HmcFDZwQdT3G39c7TdK8B1kymM4GqJD2JjpxExGiubpsFz1mpr80Ux2ehuFHf0yl+kzgA\nyHqi1DhxPsyMshn5RrlYh7I5qU2D0snNSn19DziO4j/kLwP/R9Ix5bra3Emzh5wPMxtC0sMpJpHe\nHhGTfv6apH0oTkQ4hGLCcV9E/CFtVLvmw0A1JemRwJSIuK3c7Xk28Fzgl8AHImJz0gAnmPNhZpLO\nBpZFxJ8k7UExoXYBxS/m24ALgbdOpivZSroBeH5E3FHO6fk+xR7nX1A0LA8AR+Z+SMjNipmZNYKk\nB4HHRMTtkt4JvBd4B3AdxRVbzwM+UocJpb1S3ol6epmTi4AnASdGxF2S9gW+Cvw+Il6TNNBd8GEg\naxRJBwEHUTktPyL+M01EZjaB1PHza4B3R8Tny8c3SAJ4DzBpmpWKOcDb2ofDIuKPkhYBq9KGtWtu\nVmqm/M1hlyJij/GOJSeSDgO+QHGdFVU2BzCp8mE2ibUPF8yguPZSpx9S7FmYbNo5mUIxT6XTrcCj\nJzac3edmpX4E/IbiP+YfJ44lJ5+lOAb7RoprK/j4ptnk9GZJfwTuBx5Z2fYI4L6JDym5b0n6M7Af\nMJMdr2b7BCD7CbZuVurncIr/kM+kOAXts8DFnkDKk4FX5n7nUDMbVwPAm8uf7wNmU5wp2HY0cONE\nB5XYOZXHf6w8finFpNuseYJtTUmaAvwPYB7FxY/+HVgZEd9MGlgiklYDF0bEV1LHYmZ5knQkcF9E\neK90zbhZaYDyqoQrgRcCj46IOxKHNOHK+358gWLW/88oTsd7SERcliIuMzMbOx8GqjFJjwNOK5ep\nwIeBuxOGlNIc4HnACcNs8wRbs0mmrI+3RcS2zp9Tx5Vaecbku4HzIuK3qeMZLd91uWYk7SXpZElr\nKC54Nhv4n8DjI+LdEfHntBEmswy4iOIaCw+rLG5UzCafGyiuXFv9ebJ7HcWcx9NTB7I7fBioZsqb\n9N1DccjjQuD24cZFxKTaw1LeafnZEfHr1LGYWXrtu69HxH91/pw6rtTKO9RvBJ4SEU9JHc9oec9K\n/RxIcf2A91HMat9cWe4s/5xs/n+Kmf5mZjYMSbMpLrH/euCRkl6QOKRR85yV+vF/yMP7BfDPkp5P\ncTPD6gTbjyWJyswsH28A/j0iNkr6MsV8x+xPWwYfBrKGkLSzm3BFRDx5woIxs+R8GGhHkvakuHrt\naRFxuaSjKC55MT0i/pQ2ul3znhVrhIiYjJfQNjMbrZOAB4FvAETE98o5kK8ALk4Z2Gh4zoo1Snm2\n1MzytwgzMyu8HuirnL59EcWhoOy5WbFGkDRV0kpgC/BziknISFom6d1JgzMzS6i8aOZLgAsqmy4C\nji6vQ5M1NyvWFP8MPAt4EbC1Y/1VwMkpAjKzpP4/4I5hfp6M7gH+qnqbgYj4BcVdqLO/kaEn2Foj\nSPoNcHJE/Kgyse4QoD8i9kscopmZdcl7VqwpHs3wF8jbh+Jy+2ZmVlNuVqwp/oPimGxbu0F5E3DN\nxIdjZilIeqykj5R3pu9cv6ekD0t6YprIbCx8xoQ1xXuBb0h6KsW/6zPLn59LcTdqM5scfge8iuIX\nmL6O9SdQXBTtPSmCsrHxnhVrhIhYCzybolG5HphLcVhoTkSsSxmbmU2cKCZiXkRxqm6n1wGrJvHN\nXmvNE2zNzKxRJP018J8Ud6PfJGl/ij0uz4+I/rTRWTe8Z8VqTdK5kqZ2PD4wZTxmll5EbADWAa8t\nV/0d8Gs3KvXlZsXq7n8D+3Y8/o0k3wfIzC5g+6Gg1wFfSBiLjZGbFas77eKxmU1OfcBMSS8DjgQu\nTByPjYGbFTMza5yIuBO4DFgJXBURmxKHZGPgZsXqLoBHSNqvnEQXwL7l44eWxDGaWRoXAI/Eh4Bq\nz2cDWa1J2saOV6jVcI8jYo8JDczMkpMkipua/jYiHkwdj3XPF4Wzujs6dQBmlqfymiu/SR2HjZ33\nrJiZmVnWPGfFzMzMsuZmxczMzLLmZsXMzMyy5mbFzMzMsuZmxczMzLLmZsXMzMyy5mbFzMzMsuZm\nxczMzLLmZsXMzMyy5mbFzMzMsuZmxczMzLLmZsXMzMyy9v8ALH8p4z4f2+4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9ee3c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAGvCAYAAACaZ5V7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3XucVXW9//HX2ytigSYpmpJ3pIsX4ChUokR5Id3HtETQ\nFDylKfrzTIWa6QHFTMhboHZRTFMZb3jQowYo4QUlPDKaVgxH8zIJgU4CKhOi8Pn9sdaMe9bsGYZh\nZvae4f18PPZD57s+e+3Pd1/Yn/1d3/VdigjMzMzMStVmxU7AzMzMrCkuVszMzKykuVgxMzOzkuZi\nxczMzEqaixUzMzMraS5WzMzMrKS5WDEzM7OS5mLFzMzMSpqLFTMzMytpLlbMGiFppKR1knoVO5dN\nhaTHJf2h2HlsLEmvS7ol7+/D0vfSoHZ47HGS1mXa1kma1NaPnT6WPzfW6lysmDUu0pu1n87yfBfq\nxwb3TdKPJf17Cx573XqjNlITuflzY63OxYpZ434HbBMRVcVOxDq2iHiC5L305Abe9SJgQ4uV8UDX\nDbxPSzSWmz831upcrJg1IhJrip2HdQ5t/V6S1DV9nHXFfN/6c2NtwcWKWSMKHXtP5yI8KOnLkuZL\n+pekv0n6ToH7d5d0raTXJK2W9HdJt0n6VF7MpyVNkbQ03dcLkk7N7OezaR4/kHR2+nirJM2U9Jk0\n5pJ0/zWSpkvarkA+R0t6UtL7kt6V9JCkzzXjedhe0lWSXpT0nqSVkh6RtH8mrnZexrcl/STN51+S\nHpO0V4H9niHplTTnP0r6yvpyybvv1yU9JWl5mlOlpJ9mYraSdKmkl9Pnv0rSBElb5cXUvsYjM/e9\nKG0/qhm5XJz2dZWk2YWe00JzViTtLWmapH+kz9PfJZVL+mS6fR3JCEltjutq58HUzkuR1EfSVEnv\nAE/lb2sk1xHpc/UvSc9JOjSz/VZJrxW4X719rie3gnNW0vfun9PXYrGk6yV1z8Q8nr7P+kiakz6n\nb0oa0+SLYJ3eFsVOwKyEFTr2HsA+wL3AFOBW4HTgt5Kei4iFAJK2BeYCvdO454EeQA7YFXhHUhfg\nCWBPYDLwOvBt4FZJ3SNicuaxTwG2BCYBnwIuAO5VMiH1MOBKYG/g/wFXAd+tvaOSYupWYAZwPskX\nzVnAU5IOWs+Q/Z5p3vcCrwE7AWcCj0v6XEQszcRfCKwFfg50T/O8AxiYl89/AL9Kn6Nr08d4EHgH\naPLwQVoM/A/wAnAJ8EHa7y/lxSiN+RLwa6AS+CJQRvL6HQ8QEbdKOh64RtKjEbFY0heB/wJuiogZ\n68llPPAT4CHg90BfYBbJ65QVeffbMi9uErAU+AxwDLAd8B7J6z0FmA/8Jr3r3zL7uhf4P+DHgPK2\nFZozcjgwLH28D4Czgd9LOjgi/rqe+2bb15dbvX1IGkfynM4CbiT5XJwN9Jf05YhYm3ffT5E8l/cD\ndwHfAq6U9GJEzCyQm20KIsI333wrcANOI/nS7ZXX9lra9qW8th7Av4CJeW2XpnG5JvZ/XhpzUl7b\n5sDTwEpg27TtsyQTJpcCn8iL/WnaXgFsltd+Z5rPlunf25IUAb/MPP6ngeXAr9bzPGxZoK1X+hg/\nyWs7LM3nz8Dmee3npv38XPr3FmlfngO2yIv7j/T+f1hPPrXP2/ZNxJwCfAgMzLSfkd53QF7bTkA1\nSSG3Zfp8vpr/XDfyGD2A1cADmfbL037cknlu1gKD0r8PSGO+uZ7HeC9/P3ntY9P7397ItrWZtnXp\n4x+Y17YbUAPcl9f2W+DVZu6zsdzqfW7ynqdHMnFnp3Gn5bXNSdtG5L//gCXAPS39LPvW8W8+DGS2\n4f4aEc/U/hER1cAiktGBWscDf4qIB5vYz9HA0oi4K29fa0l++X6C5Asu3z0R8X7e3/PT/94eEesy\n7VuR/FIHOIJkhOMuSTvU3kh+xc4HBjfV2Yj4sPb/JW2m5DBWTdrnvgXuckt8/EsZksMT4uPnpz+w\nI0mR9FFe3G0kRdr6rEj/+810BKWQbwELgf/L9HlOmktdnyNiGTCa5Hl6CtgfOD3zXBfyNZIv0uwI\n2HXN6ENtP4+StE0z4gsJklGj5nomIl6ou3PE34EHgCObeB5bQ+3zlH1ebiIpeL6RaX8/Iqbm5fkh\n8Cz1P1+2iXGxYrbhCh2mWA5sn/f3XiQjDE35LPBygfaFJF+on820/z3zd+0X3puNtNfms3e6vznA\n23m3t4Cvk4ywNEqJMkn/R3L4oDq97xdJiqCsbJ7LM/l8luSL9pX8oLRwebWpXFJ3k4w+3QQsS+d5\nfDvzhbsP8Hnq9/dtkgIrSIql/Me+G3gYOJjk8M/jzcij9vXJ9qOaj/tcUES8DlxNcqiuWtKMdE5H\nt2Y8br4G80ua8EqBtv8jOSTY5HtgI9U+T/+X35gWIa/S8H2efT9Dw8+XbWI8Z8Vsw61tpL0tf502\n9bjry2czki/oU4BlBeI+KtCW7yfAZcDNwMUkh5TWAb+g8A+eNn1+ImI1MEjSYJJf5UeRzMWYLemI\niIg0r5dI5qgUetx6BVU6WtSf5Hla76Tj1hARYyTdSnL67xEkI2oXShoQEUuauZt/tXZajbRv3sqP\n05Rifb6shLlYMWsbfwO+sJ6YN0hGJ7L65G1vrVwEvB0RLVkd9gSSeSRn5DcqOePo7Rbs7400n32A\nx/P2twWwB8nE2fWKiDkko0U/kvRjkrkig4E/kPR5/zSmOW4kOfT2Y5LJnP8ZEes7nFP7+uxDMjm6\nth89aOYoQET8BfgLcIWkAcAzwPdJJqNC6y6utk+Btt4kh/RqX8flJBN8s3Yv0Nbc3Gqfp97Uf562\nJHm9H23mfmwT5sNAZm1jGnCAml599BGgp6RhtQ2SNieZkPoeyZlCrWEm8C5wUVoQ1JN+uTZlLZlf\ntZK+zcdzYjbUcyRfjt/P5DOKwl+U9UgqVAj8Kc1x6/Tve4BdJX2vwP27KF2TJP37W8CJwAURMZHk\nDJTLJe29nlQeIxmVOjfTXtaMPnwyfa3z/YVkxGrrvLZVNOM5aaaBkg7Ky2E3krO8ZqajUZAUed0l\nfSEvbmfguAL7a25uj5FMdv5/mfbvAt1IzqQya5JHVszaxs9JJnneK+m3wAJgB+BY4MyIeInklM8z\nSU5V7s/Hpy4PBM6LiFUb8fh1xUVEvCfpLJKVRSsk3UVSLPQiOYwyl4ZfJPkeAi5J19F4hmQ06GQ+\nPlV1g0TER5IuJjl1eY6ku0l+YY9q5j7/S8l6JQ+T/GrfieQ07Kq0LwC3kxQgv0wPFz1NciijD8lz\nfATJc7Ej8EtgdkTcmN73HJIRmtuALzfRj2pJV5EcunmIpPg8iOSwVKERp/yC76vA9ZJqTz3eAjiV\npPiZlhe3APiapDKSM2Jei4hnm356GvVnYIakycAakucsgHF5MXcBE4DpSq4ltC3JSE+hydTNyi19\nnn5G8rrNIDlFfb/08Z8lOXvNrEkuVsw2TFPXPalrj4hVShY5uxT4JskX0VskvzLfTGNWS6pdH+VU\nkl+Zi4CREXF7Mx93vbmkj1UuaTHJGig/Ivn1vpjk7JffNrKPWleQTMIcQVIALACGpnkXWoemOfnc\nJGkzYAwwkWR+ybEkS8Wv7/DCAySTMkeRnBZbTXI4aVxEvJfuP9JRrTKS5/Y4ksMdr5Ks61I72fNG\nkn8HR+Xl9o6kM0i+sH8UEVc1lkhE/ETSv0i+0A8H/khSCD1coB/5f/+J5FTpY0hGqGrStqMyX/g/\nIDnjZzywDUkB1ZxipdBjP57mN47ktOW/AKdGRN1E8LTvxwHXkBQtr5G8Z/alYbHS7Nwi4lJJb5EU\ngteQzHv6Fcmp79k5Ks16D9mmRR+P/pmZmZmVnpKYsyLpUCVLmC9Ol2nOFYjpI+kBSSuULBc+X9Ku\nedu3lnSDpGoly2/flw7x5u9je0l3KlkufLmkm5WsNJofs5ukh5Us87xU0sT0F6CZmZkVQal8CW9L\ncgbA2RQY6lNyXZGngL8Cg0iOmY8nWRWx1nUkx99PSGN2of6xX4CpJMesh6Sxg8hbVCktSh4hGRYe\nQLIS40iS0zbNzMysCEruMJCSC2Qdl7/yp6RyYE1EnNbIfbqRTGg7KSL+O23rTbK41oCIeFZSH5Jj\ntP0i4vk05kiSY8u7RsRSSUeTTP7aOV3YCUlnkhyb/3RmtU0zMzNrB6UystKodFXKbwAvp6s8LlNy\nhdb8U0L7kYyGzK5tiIhFJGcH1F48bQCwvLZQST1GMpJzSF7MS7WFSmomySqdn2/FbpmZmVkzdYSz\ngXYkWazpApKVNM8nuabK/ZIOj4ingJ4kIy/vZu67LN1G+t+38jdGxFoll1bPj8mu8Lksb9ufssml\n1xs5kuS009XZ7WZmZtaoLiSLDs6MiH82FtQRipXa0Z/pETEp/f8XJX2J5HTBp4qTVp0j8ToBZmZm\nG+NkknmlBXWEYqWaZKGkhZn2hXy8YNNSYCtJ3TKjKzul22pjsmcHbQ58KhPzb5nH2SlvWyGvA9xx\nxx306dOnkRDrSMrKyrj22muLnYaZNcKf0c5j4cKFnHLKKZB3KYZCSr5YiYgPJf0vyXUl8u3Lx9ec\nWEBS0AwB8ifY9gLmpTHzgO0kHZQ3b2UIyaqS8/NiLpLUI2/eyhEkV7H9ayMprgbo06cPfftm10yy\njqh79+5+Lc1KmD+jnVKT0yhKolhJ1zqpvYw9wJ6SDgDeiYi/kyxdfpekp0guXHY0ycqPhwFExLuS\npgDXSFpOcl2VScDTtatBRkSlpJnATenS41sBk4HyiKgdNZlFUpTcLukCYGeSU6SvTy9nbpuApUsb\nG0Qzs1Lgz+impySKFZJLs8/h4yXFr07bbwNOj4jpkr4PXERyWfpFwPERMS9vH2UkF1y7j2Qp8RnA\n6MzjjACuJzkLaF0ae17txohYJ+kYkmuFPENyoa5bgbGt1VErfYsXLy52CmbWBH9GNz0lUaxExBOs\n5zTqiLiVpHBobPsHJFc/zV4BNT9mBXDKeh7n7ySjNraJ6tevX7FTMLMm+DO66Sn5dVbM2tvw4cOL\nnYKZNcGf0U1PSYysmJUS/0No1n6qqqqorq5ef2Ce3r17U1FR0UYZWWvr0aMHvXr12qh9uFgxM7Oi\nqKqqok+fPtTU1BQ7FWtDXbt2ZeHChRtVsLhYMcsYNWoUv/3tb4udhlmnV11dTU1Njdep6sRq11Gp\nrq52sWLWmo444ohip2C2SfE6VbY+nmBrluE5K2ZmpcXFipmZmZU0FytmZmZW0lysmGXMnTu32CmY\nmVkeFytmGRMnTix2CmZmlsdnA5ll/PznP98kFpxqjYWazNpKSxaLawub2ufkiSeeYPDgwTz++OMM\nGjSo2OnUcbFilqeqqooDDzqQ1f9q8mrlnUKXbbqwqHLRJvUPsXUMVVVV9O7dh9Wri79YXJcuXVm0\nqGULmt12222MGjWq4LYLL7yQK664YmPTaxOSip1CAy5WzPJUV1cnhcrxQI9iZ9OGqmH1/as3eqEm\ns7ZQXV2dFip3AMVcLG4hq1dv3IJmkhg/fjy77757vfYvfOELrZDfpsPFilkhPYBdip2E2aauD9Dx\nF4s76qijvOjdRvIEWzMzsyK644476N+/P127dmWHHXZg+PDhvPnmm/ViDj/8cPbff39eeuklDj/8\ncLbddlv22Wcfpk2bBiRzTQYMGEDXrl3Zb7/9mD17dr37V1VVcfbZZ7PffvvRtWtXevTowYknnsgb\nb7zRrBznz5/PUUcdxXbbbce2227L4YcfzjPPPNM6T0AzuFgxMzNrQytXruSf//xnvVutn/70p5x2\n2mn07t2ba6+9lrKyMmbPns1hhx3Gu+++WxcniXfeeYdjjz2WAQMG8POf/5wuXbowfPhw7rnnHoYP\nH84xxxzDhAkTWLVqFd/+9rdZtWpV3f3/93//lz/+8Y8MHz6cyZMnc9ZZZzF79mwGDx7M6tVNz9H7\nwx/+wGGHHcb777/PuHHj+NnPfsbKlSv56le/ynPPPdf6T1gBPgxkZmbWRiKCIUOG1GuTxNq1a3nj\njTcYN24cV1xxBRdccEHd9uOPP54DDzyQG2+8kQsvvLCu/R//+Afl5eWceOKJAHzta19jv/324+ST\nT2bevHn0798fgP32248jjzySadOmceqppwJwzDHHcMIJJ9TLo7bwmTZtGieffHKjfTjrrLMYMmQI\nDz/8cF3bmWeeyec+9zkuvvhiZsyY0cJnp/lcrJiZmbURSdx4443ss88+Dbbdf//9RATf/va36422\n7Ljjjuyzzz7MmTOnXrHyiU98oq5QAdh3333Zbrvt2HXXXesKFYBDDjkEgFdffbWubeutt677/48+\n+oh3332XPffck+22246KiopGi5UXXniBl19+mUsuuaRejrVF2B133LEhT0eLuVgxMzNrQ//2b/9W\ncILtK6+8wrp169h7770bbJPEVlttVa9t1113bRDXvXt3dtttt3pt3bp1A2D58uV1batXr+aKK67g\n1ltvZfHixURE3eOsXLmy0dxffvllgLoRmqzNNtuMlStX0r1790b30RpcrJiZmRXBunXr2GyzzZgx\nYwabbdZwCuknPvGJen9vvvnmBffTWHttQQJwzjnncNttt1FWVsaAAQPo3r07khg2bBjr1q1rMkeA\nq6++mgMOOKBgTDbPtuBixczMrAj22msvIoLdd9+94OhKa5o2bRojR46sdzmRDz74gBUrVqw3R4BP\nfvKTfPWrX23THJvis4HMzMyK4Pjjj2ezzTbj0ksvLbj9nXfeabXH2nzzzRuMoEyaNIm1a9c2eb9+\n/fqx1157cdVVV9U7u6hWe10SwSMrZmZWohZ2+MfPPxSTteeee3L55Zdz0UUX8dprr3HcccfxyU9+\nkldffZXp06dz5pln8oMf/GCjc4DkbKDbb7+dbt268bnPfY558+Yxe/ZsevRouFR3fs6SuPnmmxk6\ndCif//znGTVqFJ/5zGdYvHgxc+bMoXv37jzwwAOtkmNTXKyYmVlJ6dGjB126dGX16lOKnQpdunQt\n+IXeXOu7zs4FF1xQt8bKZZddBsBuu+3GUUcdRS6XW+++JDWrfdKkSWyxxRZMnTqV1atX85WvfIXH\nHnuMI488ssH9s38fdthhzJs3j/Hjx3PDDTfw/vvv07NnTw455BDOPPPMpp+AVuJixczMSkqvXr1Y\ntGhhh7/q8mmnncZpp5223rjjjjuO4447rsmYOXPmFGzPPz05X/bwTrdu3bj55pvXe//DDjus4KGh\n/fffn3vvvbfJHNuSixUzMys5vXr18kU2rY4n2JqZmVlJc7FiZmZmJa0kihVJh0p6UNJiSesk5ZqI\n/VUa8/8y7VtLukFStaT3JN0nacdMzPaS7pS0UtJySTdL2jYTs5ukhyWtkrRU0kRJJfE8mZmZbYpK\n5Ut4W+AF4Gyg0fO8JH0TOARYXGDzdcA3gBOAQcAuwLRMzFSgDzAkjR0E/Dpv/5sBj5DM5RkAnAaM\nBC7b8C6ZmZlZayiJCbYRMQOYAaBGzvOS9BngF8CRJAVF/rZuwOnASRHxRNo2Clgo6eCIeFZSn/S+\n/SLi+TTmXOBhST+KiKXp9v2AwRFRDbwk6RLgSknjIuKjVu+8mZmZNalURlaalBYwvwMmRkShVXr6\nkRRes2sbImIRUAUMTJsGAMtrC5XUYyQjOYfkxbyUFiq1ZgLdgc+3QlfMzMxsA3WIYgW4EFgTEdc3\nsr1nuv3dTPuydFttzFv5GyNiLfBOJmZZgX2QF2NmZmbtqOSLFUn9gP8HjCp2Lk0ZOnQouVyu3m3g\nwIFMnz69XtysWbMarEoIMHr0aKZMmVKvraKiglwu12BhpLFjxzJhwoR6bVVVVeRyOSorK+u1T548\nmTFjxtRrq6mpIZfLMXfu3Hrt5eXljBrV8GkeNmzYJtcP5gIVmbYlJLOespfHmJPG51uRxr6daZ8P\nzMq0rUlj38i0vwRMp6F7abgK+CvpPrIepnA/ZjQMLeXXo7O8r9yP+v147bXXGuzbOq/y8vK678ae\nPXuSy+UoKytr1n3V1HULikHSOuC4iHgw/fs84GrqT7zdHFgHVEXEnpIGkxzS2T5/dEXS68C1EfGL\ndA7LVRGxQ972zYHVwLci4gFJlwLHRkTfvJjdgVeBgyLiTwXy7QssWLBgAX379s1utg6moqKCfv36\nwRkkU7Q7qyXAb8DvWyum2s+b34ed1/pe47p/c5P5pNmfVnVKfmSFZK7K/sABebclwESSCbEAC4CP\nSM7yAUBSb6AXMC9tmgdsJ+mgvH0PAUTye7c25ouS8i8EcQSwEvhr63XJzMzMmqskzgZK1zrZm6Rw\nANhT0gHAOxHxd2B5Jv5DYGlEvAwQEe9KmgJcI2k58B4wCXg6Ip5NYyolzQRuknQWsBUwGShPzwSC\nZID+r8Dtki4AdgbGA9dHxIdt1X8zM7MNtfvuu/PVr36VW265pdiptLmSKFaA/iRH/iO9XZ2230Zy\nSnJWoWNXZcBa4D5ga5Kj8qMzMSOA60kOGa1LY8+r22nEOknHAL8EniGZnXArMLYFfTIzsxaqqqrq\n8BcyvO222+rmB82dO5cvfelLDWJ22203Fi9ezDHHHMODDz64Qftf3xWdO5OSKFbStVGafUgqIvYs\n0PYBcG56a+x+K4AmrzmejuQc09xczMysdVVVVdF7v96s/tfqYqdCl226sKhy0UZdVHGbbbZh6tSp\nDYqVJ554gsWLF9OlS5eNTbPTK4lixczMrFZ1dXVSqBwP9FhveBsmAqvvX011dfVGFStDhw7l3nvv\nZdKkSWy22ce/y6dOnUr//v1LYgSp1HWECbZmZrYp6kFyVl6xbq1QKEli+PDh/POf/+TRRx+ta//w\nww+57777GDFiBNmzcq+66iq+/OUv06NHD7p27Ur//v2ZNi179ZjCVq5cyX/+53/Sq1cvunTpwj77\n7MPEiRMbPEZH42LFzMysDe2+++4MGDCA8vLyurZHHnmEd999l5NOOqlB/KRJk+jbty/jx4/nZz/7\nGVtuuSUnnngiv//975t8nH/9618MGjSIqVOnMnLkSCZPnsxXvvIVfvzjH/PDH/6w1fvVnnwYyMzM\nrI2NGDGCiy66iA8++ICtt96aqVOncthhh9GzZ8PF0V9++WW23nrrur/POeccDjroIK655hqOPvro\nRh/j6quv5rXXXuOFF15gzz2TqZ3f+9732Hnnnbnqqqv44Q9/yGc+85nW71w78MiKmZlZGzvxxBOp\nqanhoYce4v333+ehhx7i5JNPLhibX6isWLGC5cuXc+ihh1JR0eiaaQDcd999HHrooXTv3p1//vOf\ndbchQ4bw0Ucf8eSTT7Zqn9qTR1bMzMzaWI8ePfja177G1KlTWbVqFevWreNb3/pWwdiHHnqIn/70\np7zwwgt88MEHde35k3MLefnll3nppZf49Kc/3WCbJN56660C9+oYXKyYmZm1gxEjRvC9732Pf/zj\nHxx99NF88pOfbBDz1FNP8e///u8cfvjh/PKXv2TnnXdmyy235JZbbqk356WQdevW8fWvf50LLrig\n4ITafffdt9X60t5crJiZmbWDb37zm5x55pnMnz+fu+++u2DMtGnT2GabbZg5cyZbbPHxV3T2wpGF\n7LXXXrz//vsMHjy41XIuFZ6zYmZm1g623XZbfvWrXzFu3DiOPfbYgjFbbLEFkvjoo4/q2l5//XUe\neOCB9e7/xBNPZN68ecyalb20e3JK89q1a1uefJF5ZMXMzEpTsddKa4XHzx6O+c53vtNk/De+8Q2u\nueYajjzySEaMGMGyZcu48cYb2WeffXjxxRebvO+YMWN48MEHOeaYYxg5ciT9+vVj1apVvPjii9x/\n//28/vrrfOpTn9roPhWDixUzMyspPXr0oMs2XVh9f2kst9+jR8tXh2vO9Xsk1cUNHjyYW265hSuv\nvJKysjL22GMPJk6cyGuvvdagWMm/HyTL+j/55JNcccUV3Hvvvdx+++1069aNfffdl8suu4zu3bu3\nuB/F5mLFzMxKSq9evVhUuagklqHfmAsZnnbaaZx22mnrjXv11Vfr/T1y5EhGjhzZIG7s2PrX1M3e\nD6Br165cfvnlXH755RuWbIlzsWJmZiWnV69eG3U9HutcPMHWzMzMSpqLFTMzMytpLlbMzMyspLlY\nMTMzs5LmYsXMzMxKmosVMzMzK2kuVszMzKykeZ0VMzMrqoULFxY7BWsjrfXaulgxM7Oi6NGjB127\nduWUU04pdirWhrp27bpRlywAFytmZlYkvXr1YuHChSWxrL61nY25ZEEtFytmZlY0XlbfmsMTbM3M\nzKykuVgxM7MOZcyYMcVOwdqZixUzM+tQfNho01MSxYqkQyU9KGmxpHWScnnbtpA0QdKLkt5PY26T\ntHNmH1tLukFStaT3JN0nacdMzPaS7pS0UtJySTdL2jYTs5ukhyWtkrRU0kRJJfE8mZkZnHvuucVO\nwdpZqXwJbwu8AJwNRGZbV+BA4FLgIOCbQG/ggUzcdcA3gBOAQcAuwLRMzFSgDzAkjR0E/Lp2Y1qU\nPEIy8XgAcBowErhsI/pmZmZmG6EkzgaKiBnADABJymx7Fzgyv03SOcB8SbtGxJuSugGnAydFxBNp\nzChgoaSDI+JZSX3S/fSLiOfTmHOBhyX9KCKWptv3AwZHRDXwkqRLgCsljYuIj9ruWTAzM7NCSmVk\nZUNtRzICsyL9ux9J4TW7NiAiFgFVwMC0aQCwvLZQST2W7ueQvJiX0kKl1kygO/D5Vu6DmZm1QGVl\nZbFTsHbW4YoVSVsDVwJTI+L9tLknsCYdhcm3LN1WG/NW/saIWAu8k4lZVmAf5MWYmVkRnX/++cVO\nwdpZhypWJG0B3EsyGnJ2kdMxM7MiuP7664udgrWzDlOs5BUquwFH5I2qACwFtkrnruTbKd1WG5M9\nO2hz4FOZmJ0K7IO8mIKGDh1KLperdxs4cCDTp0+vFzdr1ixyuVyD+48ePZopU6bUa6uoqCCXyzVY\ninrs2LFMmDChXltVVRW5XK7B8OjkyZMbrElQU1NDLpdj7ty59drLy8sZNWpUg9yGDRu2yfWDuUBF\npm0JyRQa5McYAAAgAElEQVTtVZn2OWl8vhVp7NuZ9vnArEzbmjT2jUz7S8B0GroXyF4b7JV0H1kP\nU7gfMxqGlvLr0VneV+5H6/SjV69enaIf0Dlej+b2o7y8vO67sWfPnuRyOcrKyhrcpxBFZE++KS5J\n64DjIuLBvLbaQmVPksmv72Tu043ka+GkiPjvtK03yT/pA9IJtvsBfwH6502wPYLk7J9dI2KppKOA\n/wF2rp23IukMYAKwY0R8WCDfvsCCBQsW0Ldv31Z9Lqz9VVRU0K9fPziD5HyyzmoJ8Bvw+9bMiqnu\n39zk5JfsT6s6JXE2ULrWyd5A7ZlAe0o6gGQ+yT9ITkE+EDgG2FJS7WjHOxHxYUS8K2kKcI2k5cB7\nwCTg6Yh4FiAiKiXNBG6SdBawFTAZKE/PBILkN+9fgdslXQDsDIwHri9UqJiZmVnbK5XDQP2B54EF\nJPNRriYZvL4U+AxwLLAryVosS0gKmCV8fKYPQBnwEHAf8Hi6/YTM44wAKknOAnoIeBI4s3ZjRKwj\nKYjWAs8AvwNuBca2TjfNzGxjZQ9jWOdXEiMr6dooTRVO6y2qIuID4Nz01ljMCuCU9ezn7yQFi5mZ\nlaCamppip2DtrFRGVszMzJrl0ksvLXYK1s5crJiZmVlJc7FiZmZmJc3FipmZdSjZtUOs83OxYmZm\nHcrpp59e7BSsnblYMTOzDmXcuHHFTsHamYsVMzPrULzq8qbHxYqZmZmVNBcrZmZmVtJcrJiZWYeS\nvcKwdX4uVszMrEOpqGj04rzWSblYMTOzDuWGG24odgrWzlysmJmZWUlzsWJmZmYlzcWKmZmZlTQX\nK2Zm1qHkcrlip2DtzMWKmZl1KOecc06xU7B25mLFzMw6lCOOOKLYKVg7c7FiZmZmJc3FipmZmZU0\nFytmZtahTJ8+vdgpWDtzsWJmZh1KeXl5sVOwduZixczMOpS777672ClYO3OxYmZmZiXNxYqZmZmV\nNBcrZmZmVtJcrJiZWYcyatSoYqdg7czFipmZdShewXbTUxLFiqRDJT0oabGkdZIaXKVK0mWSlkiq\nkfSopL0z27eWdIOkaknvSbpP0o6ZmO0l3SlppaTlkm6WtG0mZjdJD0taJWmppImSSuJ5MjMzGD58\neLFTsHZWKl/C2wIvAGcDkd0o6QLgHOAM4GBgFTBT0lZ5YdcB3wBOAAYBuwDTMruaCvQBhqSxg4Bf\n5z3OZsAjwBbAAOA0YCRw2Ub2z8zMzFpoi2InABARM4AZAJJUIOQ8YHxEPJTGnAosA44D7pHUDTgd\nOCkinkhjRgELJR0cEc9K6gMcCfSLiOfTmHOBhyX9KCKWptv3AwZHRDXwkqRLgCsljYuIj9rsSTAz\nM7OCSmVkpVGS9gB6ArNr2yLiXWA+MDBt6k9SeOXHLAKq8mIGAMtrC5XUYyQjOYfkxbyUFiq1ZgLd\ngc+3UpfMzGwjzJ07t9gpWDsr+WKFpFAJkpGUfMvSbQA7AWvSIqaxmJ7AW/kbI2It8E4mptDjkBdj\nZmZFNHHixGKnYO2sIxQrHcLQoUPJ5XL1bgMHDmxwwa1Zs2aRyzWYP8zo0aOZMmVKvbaKigpyuRzV\n1dX12seOHcuECRPqtVVVVZHL5aisrKzXPnnyZMaMGVOvraamhlwu1+DXSXl5ecFTAocNG7bJ9YO5\nQEWmbQnJrKdVmfY5aXy+FWns25n2+cCsTNuaNPaNTPtLQKHrtd0LLMy0vZLuI+thCvdjRsPQUn49\nOsv7yv1onX7cddddnaIf0Dlej+b2o7y8vO67sWfPnuRyOcrKyhrcpxBFNJjPWlSS1gHHRcSD6d97\nAH8DDoyIF/PiHgeej4gySYNJDulsnz+6Iul14NqI+EU6h+WqiNghb/vmwGrgWxHxgKRLgWMjom9e\nzO7Aq8BBEfGnAvn2BRYsWLCAvn37ZjdbB1NRUUG/fv2Sqdy7FDubNrQE+A34fWtmxVT3b24ynzT7\n06pOyY+sRMRrwFKSM3gASCfUHgI8kzYtAD7KxPQGegHz0qZ5wHaSDsrb/RBAJL93a2O+KKlHXswR\nwErgr63UJTMzM9sAJXE2ULrWyd4khQPAnpIOAN6JiL+TnJZ8saRXgNeB8cCbwAOQTLiVNAW4RtJy\n4D1gEvB0RDybxlRKmgncJOksYCtgMlCengkEyQD9X4Hb09Old04f6/qI+LBNnwQzMzMrqFRGVvoD\nz5OMkARwNcmR9ksBImIiSWHxa5JRkG2AoyNiTd4+yoCHgPuAx0kGuk/IPM4IoJLkkNFDwJPAmbUb\nI2IdcAywlmTU5nfArcDYVuqnmZltpOw8Cuv8SmJkJV0bpcnCKSLGAeOa2P4BcG56ayxmBXDKeh7n\n7yQFi5mZlaBevXoVOwVrZ6UysmJmZtYs557b6G9S66RKYmTFOoaqqqoGp8F1NgsXZs8JNjOzYnOx\nYs1SVVVF7959WL26ptipmJnZJsbFijVLdXV1WqjcQXItyM7qEeCSYidhZk2orKxkv/32K3Ya1o5c\nrNgG6gN05kXEfBjIrNSdf/75PPjgg8VOw9qRJ9iamVmHcv311xc7BWtnLlbMzKxD8anLm54WFSuS\ndpO0a97fB0u6TtIZrZeamZmZWctHVqYCgwEk9QQeBQ4Gfirpv1opNzMzM7MWFytfAJ5N//9E4M8R\n8SXgZGBkK+RlZmZW0IQJE4qdgrWzlhYrWwIfpP//NaB2WnYlycX/zMzM2kRNjdd72tS0tFj5C/B9\nSYcCXwdmpO27AP9sjcTMzMwKufTSS4udgrWzlhYrF5BcrfhxoDwi/pS25/j48JCZmZnZRtvgReEk\nCXgV6AVsERHL8zb/BvD4nJmZmbWaloysCHgF6JkpVIiI1yPirVbJzMzMrIDOfkFVa2iDi5WIWAe8\nDOzQ+umYmZk17fTTTy92CtbOWjpn5ULg55K+0JrJmJmZrc+4ceOKnYK1s5ZeyPB3QFfgT5LWAP/K\n3xgRn9rYxMzMzArp27czX0zVCmlpsfKfrZqFmZmZWSNaVKxExG2tnYiZmZlZIS2+6rKkvSRdLqlc\n0o5p29GSPt966ZmZmdU3ZcqUYqdg7aylV10+DHgJOAQ4HvhEuukAwEsLmplZm6moqCh2CtbOWjqy\nciVwcUR8HViT1/4HYMBGZ2VmZtaIG264odgpWDtrabHyReC/C7S/BfRoeTpmZmZm9bW0WFlB4asr\nHwQsbnk6ZmZmZvW1tFi5C5ggqScQwGaSvgxcRbIGi5mZmVmraGmxchFQCfydZHLtX4EngWeAy1sn\nNTMzs4ZyuVyxU7B21tJ1VtYA35M0HvgCScHyfES83JrJmZmZZZ1zzjnFTsHaWYvXWQGIiCrg98C9\nbVmoSNpM0nhJr0qqkfSKpIsLxF0maUka86ikvTPbt5Z0g6RqSe9Juq92jZi8mO0l3SlppaTlkm6W\ntG1b9c3MzDbMEUccUewUrJ1tzKJw/yHpz8BqYLWkP0v6buulVs+FwJnA2cB+wPnA+ZLqymtJFwDn\nAGcABwOrgJmStsrbz3XAN4ATgEHALsC0zGNNBfoAQ9LYQcCvW79LZmZm1hwtOgwk6TLgB8BkYF7a\nPBC4VlKviPivVsqv1kDggYiYkf5dJWkESVFS6zxgfEQ8lOZ4KrAMOA64R1I34HTgpIh4Io0ZBSyU\ndHBEPCupD3Ak0C8ink9jzgUelvSjiFjayv0yMzOz9WjpyMpZwPci4scR8WB6+zHJqMbZrZdenWeA\nIZL2AZB0APBl4JH07z2AnsDs2jtExLvAfJJCB6A/SXGWH7MIqMqLGQAsry1UUo+RnPF0SKv3yszM\nNtj06dOLnYK1s5YWK1sCzxVoX0DLr+TclCuBu4FKSWvSx7kuIu5Kt9eeQr0sc79l6TaAnYA1aRHT\nWExPkoXt6kTEWuCdvBgzMyui8vLyYqdg7aylxcrtJKMrWWcAd7Y8nUYNA0YAJ5EsPHcaMEbSd9rg\nsVpk6NCh5HK5ereBAwc2+AUwa9asgqfdjR49usHFuSoqKsjlclRXV9drHzt2LBMmTKjXVlVVRS6X\no7Kysl775MmTGTNmTL22mpoacrkcc+fOrddeXl7OqFGjGuQ2bNgw5syZk2mdBRQ6fXA0kL3IWEUa\nW51pHwtMyLRVpbGVmfbJwJhMW00aOzfTXg407EfyNsr+ImukH3PTtPMtIZnRtCrTPqdACivS2Lcz\n7fPTh8y3Jo19I9P+UoF0Ae4FFmbaXkn3kfUwhfsxo2FoMd5XneXz4X60bz/uvvvuTtEP6ByvR3P7\nUV5eXvfd2LNnT3K5HGVlZQ3uU4gionmB0jV5f24BjCT5Zvlj2nYI0Av4XUSc26ydNpOkKuBnEfHL\nvLafACdHxOfSw0B/Aw6MiBfzYh4nOaW6TNJgkkM62+ePrkh6Hbg2In6RzmG5KiJ2yNu+Ockk4m9F\nxAMFcusLLFiwYAF9+/ZtzW6XlIqKCvr160cyqNV5+5nU2qckZfcuxc6lDS0BfgOd/X1rZqXt4+8W\n+kVEo1eo3JCRlYPybl8k+dZ6G9grvVWT/Ib7fAtzbkpXYG2mbR1p/hHxGrCU5AweANIJtYeQzHch\nzfejTExvkgKrdpLwPGA7SQflPc4QQCS/ic3MzKydNXt+SUQMbstE1uN/gIslvQn8heSnfRlwc17M\ndWnMK8DrwHjgTeABSCbcSpoCXCNpOfAeMAl4OiKeTWMqJc0EbpJ0FrAVyfGHcp8JZGZmVhwbtShc\nOzoHuA+4gWRp/4nAL4G6U6QjYiJJYfFrklGQbYCj09V2a5UBD6X7epxkMPyEzGONIJkw8Vga+yTJ\nGi9mZlYCCs2NsM6tpeusdAHOBQYDO5IpeiKiVQ+CR8QqknVdfrCeuHHAuCa2f0CSd6NzaiJiBXBK\nS/I0M7O25xVsNz0tPc14CnAEyQjFsySnDZuZmbW54cOHFzsFa2ctLVaOAYZGxNOtmYyZmZlZVkvn\nrCwmmaBqZmZm1qZaWqz8EJgg6bOtmYyZmdn6ZBcks86vpcXKc0AX4FVJ70l6J//WivmZmZnVM3Hi\nxGKnYO2spXNWyoHPABeRXFvHE2zNzKxd3HXXXesPsk6lpcXKl4CBEfGn1kzGzMxsfbp27VrsFKyd\ntfQwUCXJomtmZmZmbaqlxcqFwNWSDpe0g6Ru+bfWTNDMzMw2bS0tVmYAA4HZwFvA8vS2Iv2vmZlZ\nmxgzZkyxU7B21tI5K8W8qKGZmW3CevXqVewUrJ21qFiJiCdaOxEzM7PmOPfcRi/vZp1USy9kOKip\n7RHxZMvSMTMzM6uvpYeBHi/Qlr/WyuYt3K+ZmZlZPS2dYLt95rYjcBTwvyRXYzYzM2sTlZWVxU7B\n2lmLipWIWJm5VUfEo8AFgNdBNjOzNnP++ecXOwVrZy0dWWnMMqB3K+/TzMyszvXXX1/sFKydtXSC\n7f7ZJmBnksXiXtjYpMzMzBrjU5c3PS2dYPsCyYRaZdr/CJy+URmZmZmZ5WlpsbJH5u91wNsRsXoj\n8zEzM2tSVVUV1dXVxU6jzfXo0cOjSKmWLgr3hqQhwBCSM4E2A5BUu92jK2Zm1uqqqqrYa6+9+Oij\nj4qdSpvrsk0XFlUucsFCy+esjAX+C3gO+Af111gxMzNrE9XV1UmhcjzQo9jZtKFqWH3/aqqrq12s\n0PLDQN8HRkbE7a2ZjJmZWbP0AHYpdhLWXlp66vJWwDOtmYiZmZlZIS0tVm4GRrRmImZmZmaFtPQw\nUBfgDElfA14EPszfGBE/2NjEzMzMzKDlxcr+fLz42xcy2zzZ1szMzFpNS09dHtzaiZiZmZkV0trX\nBmozknaRdLukakk1kv4kqW8m5jJJS9Ltj0raO7N9a0k3pPt4T9J9knbMxGwv6U5JKyUtl3SzpG3b\no49mZmbWUIcoViRtBzwNfAAcCfQBfggsz4u5ADgHOAM4GFgFzJS0Vd6urgO+AZwADCI58W1a5uGm\npvsfksYOAn7d6p0yMzOzZmnpnJX2diFQFRHfzWt7IxNzHjA+Ih4CkHQqyVWgjwPukdSN5LpFJ0XE\nE2nMKGChpIMj4llJfUiKoX4R8Xwacy7wsKQfRcTSNuyjmZmZFdAhRlaAY4HnJN0jaZmkCkl1hYuk\nPYCewOzatoh4F5gPDEyb+pMUZ/kxi4CqvJgBwPLaQiX1GMmk4UNavVdmZma2Xh2lWNkTOAtYBBwB\n/BKYJOk76faeJAXFssz9lqXbAHYC1qRFTGMxPYG38jdGxFrgnbwYMzMza0cdpVjZDFgQEZdExJ8i\n4ibgJpJl/0vC0KFDyeVy9W4DBw5k+vTp9eJmzZpFLpdrcP/Ro0czZcqUem0VFRXkcrkGVxcdO3Ys\nEyZMqNdWVVVFLpejsrKyXvvkyZMZM2ZMvbaamhpyuRxz586t115eXs6oUaMa5DZs2DDmzJmTaZ0F\nNOwHjAamZNoq0tjsVVLHAhMybVVpbGWmfTIwJtNWk8bOzbSXAw37AcOA6Zm2RvoxN0073xKSGU2r\nMu1zCqSwIo19O9M+P33IfGvS2OyBzZcKpAtwL7Aw0/ZKuo+shyncjxkNQ4vxvuosnw/3o/37wSu0\n3eejFD7njzVsKuXXoznvq/Ly8rrvxp49e5LL5SgrK2vY0QIUUfrLokh6HZgVEWfktX0f+ElE7JYe\nBvobcGBEvJgX8zjwfESUSRpM8vJvnz+6ku772oj4RTqH5aqI2CFv++bAauBbEfFAgdz6AgsWLFhA\n3759s5s7jYqKCvr16wcsADpvP+FO4JRkmnZnvu7IEuA30Nnft9b51P1b5M9op/Dxdwv9IiJbOtbp\nKCMrTwO9M229SWvUiHgNWEpyBg8A6YTaQ/j4GkYLgI8yMb2BXsC8tGkesJ2kg/IeZwggklrZzMzM\n2llHORvoWuBpST8G7iEpQr4LfC8v5jrgYkmvAK8D44E3gQcgmXAraQpwjaTlwHvAJODpiHg2jamU\nNBO4SdJZJBdsnAyU+0wgMzOz4ugQxUpEPCfpm8CVwCXAa8B5EXFXXsxESV1J1kTZDngKODoi1uTt\nqgxYC9wHbE1y5H505uFGANeTHDJal8ae1xb9MjMzs/XrEMUKQEQ8AjyynphxwLgmtn8AnJveGotZ\nAZzSoiTNzMys1XWUOStmZma2iXKxYmZmZiXNxYqZmZmVNBcrZmZmVtJcrJiZmVlJc7FiZmZmJc3F\nipmZmZU0FytmZmZW0lysmJmZWUlzsWJmZmYlzcWKmZmZlTQXK2ZmZlbSXKyYmZlZSXOxYmZmZiXN\nxYqZmZmVNBcrZmZmVtJcrJiZmVlJc7FiZmZmJc3FipmZmZU0FytmZmZW0lysmJmZWUlzsWJmZmYl\nzcWKmZmZlTQXK2ZmZlbSXKyYmZlZSXOxYmZmZiXNxYqZmZmVtA5ZrEi6UNI6Sddk2i+TtERSjaRH\nJe2d2b61pBskVUt6T9J9knbMxGwv6U5JKyUtl3SzpG3bo19mZmbWUIcrViT9G3AG8KdM+wXAOem2\ng4FVwExJW+WFXQd8AzgBGATsAkzLPMRUoA8wJI0dBPy61TtiZmZmzdKhihVJnwDuAL4LrMhsPg8Y\nHxEPRcSfgVNJipHj0vt2A04HyiLiiYh4HhgFfFnSwWlMH+BI4D8i4rmIeAY4FzhJUs+276GZmZll\ndahiBbgB+J+I+EN+o6Q9gJ7A7Nq2iHgXmA8MTJv6A1tkYhYBVXkxA4DlaSFT6zEggENatSdmZmbW\nLFsUO4HmknQScCBJ0ZHVk6SgWJZpX5ZuA9gJWJMWMY3F9ATeyt8YEWslvZMXY2ZmZu2oQxQrknYl\nmW/ytYj4sNj5mJmZWfvpKIeB+gGfBiokfSjpQ+Aw4DxJa0hGR0QyepJvJ2Bp+v9Lga3SuStNxWTP\nDtoc+FReTEFDhw4ll8vVuw0cOJDp06fXi5s1axa5XK7B/UePHs2UKVPqtVVUVJDL5aiurq7XPnbs\nWCZMmFCvraqqilwuR2VlZb32yZMnM2bMmHptNTU15HI55s6dW6+9vLycUaNGNcht2LBhzJkzJ9M6\nC2jYDxgNTMm0VaSx1Zn2scCETFtVGluZaZ8MjMm01aSxczPt5STTkbKGAdMzbY30Y26adr4lJNOv\nV2Xa5xRIYUUa+3amfX76kPnWpLFvZNpfKpAuwL3AwkzbK+k+sh6mcD9mNAwtxvuqs3w+3I/27wev\n0Hafj1L4nD/WsKmUX4/mvK/Ky8vrvht79uxJLpejrKysYUcLUEQ0K7CY0lOHP5tpvpXkLXllRCyU\ntAT4eURcm96nG0kRc2pE3Jv+/TZwUkT8dxrTO93HgIh4VtJ+wF+A/rXzViQdATwC7BoRDQoWSX2B\nBQsWLKBv376t3vdSUVFRQb9+/YAFQOftJ9wJnJKcU7ZLsXNpQ0uA30Bnf99a51P3b5E/o53Cx98t\n9IuIbOlYp0McBoqIVcBf89skrQL+GRG1NfR1wMWSXgFeB8YDbwIPpPt4V9IU4BpJy4H3gEnA0xHx\nbBpTKWkmcJOks4CtSH7SlxcqVMzMzKztdYhipRH1hoQiYqKkriRromwHPAUcHRFr8sLKgLXAfcDW\nJIPhozP7HQFcTzIIty6NPa8tOmBmZmbr12GLlYj4aoG2ccC4Ju7zAcm6Kec2EbMCOGXjMzQzM7PW\n0FEm2JqZmdkmysWKmZmZlTQXK2ZmZlbSXKyYmZlZSXOxYmZmZiXNxYqZmZmVNBcrZmZmVtJcrJiZ\nmVlJc7FiZmZmJc3FipmZmZU0FytmZmZW0lysmJmZWUlzsWJmZmYlzcWKmZmZlTQXK2ZmZlbSXKyY\nmZlZSXOxYmZmZiXNxYqZmZmVNBcrZmZmVtJcrJiZmVlJc7FiZmZmJc3FipmZmZU0FytmZmZW0lys\nmJmZWUlzsWJmZmYlzcWKmZmZlTQXK2ZmZlbSXKyYmZlZSesQxYqkH0t6VtK7kpZJ+m9J+xaIu0zS\nEkk1kh6VtHdm+9aSbpBULek9SfdJ2jETs72kOyWtlLRc0s2Stm3rPpqZmVlhHaJYAQ4FJgOHAF8D\ntgRmSdqmNkDSBcA5wBnAwcAqYKakrfL2cx3wDeAEYBCwCzAt81hTgT7AkDR2EPDr1u+SmZmZNccW\nxU6gOSJiaP7fkkYCbwH9gLlp83nA+Ih4KI05FVgGHAfcI6kbcDpwUkQ8kcaMAhZKOjginpXUBzgS\n6BcRz6cx5wIPS/pRRCxt466amZlZRkcZWcnaDgjgHQBJewA9gdm1ARHxLjAfGJg29ScpzvJjFgFV\neTEDgOW1hUrqsfSxDmmLjpiZmVnTOlyxIkkkh3PmRsRf0+aeJAXFskz4snQbwE7AmrSIaSymJ8mI\nTZ2IWEtSFPXEzMzM2l2HK1aAG4HPAScVO5F8Q4cOJZfL1bsNHDiQ6dOn14ubNWsWuVyuwf1Hjx7N\nlClT6rVVVFSQy+Worq6u1z527FgmTJhQr62qqopcLkdlZWW99smTJzNmzJh6bTU1NeRyOebOnVuv\nvby8nFGjRjXIbdiwYcyZMyfTOgto2A8YDUzJtFWksdWZ9rHAhExbVRpbmWmfDIzJtNWksXMz7eVA\nw37AMGB6pq2RfsxN0863hGRG06pM+5wCKaxIY9/OtM9PHzLfmjT2jUz7SwXSBbgXWJhpeyXdR9bD\nFO7HjIahxXhfdZbPh/vR/v3gFdru81EKn/PHGjaV8uvRnPdVeXl53Xdjz549yeVylJWVNexoAYqI\nZgWWAknXA8cCh0ZEVV77HsDfgAMj4sW89seB5yOiTNJgkpd/+/zRFUmvA9dGxC/SOSxXRcQOeds3\nB1YD34qIBwrk1BdYsGDBAvr27du6HS4hFRUV9OvXD1gAdN5+wp3AKck07V2KnUsbWgL8Bjr7+9Y6\nn7p/i/wZ7RQ+/m6hX0RkS8c6HWZkJS1U/h0YnF+oAETEa8BSkjN4auO7kcwzeSZtWgB8lInpDfQC\n5qVN84DtJB2Ut/shgEhqZTMzM2tnHeJsIEk3AsNJxutXSdop3bQyIlan/38dcLGkV4DXgfHAm8AD\nkEy4lTQFuEbScuA9YBLwdEQ8m8ZUSpoJ3CTpLGArkuMP5T4TyMzMrDg6RLECfJ9kAu3jmfZRwO8A\nImKipK4ka6JsBzwFHB0Ra/Liy4C1wH3A1iRH7kdn9jkCuJ7kkNG6NPa8VuyLmZmZbYAOUaxERLMO\nV0XEOGBcE9s/AM5Nb43FrABO2bAMzczMrK10mDkrZmZmtmlysWJmZmYlzcWKmZmZlTQXK2ZmZlbS\nXKyYmZlZSXOxYmZmZiXNxYqZmZmVNBcrZmZmVtJcrJiZmVlJ6xAr2JqZ2fpVVVVRXV1d7DTa1MKF\nC4udghWBixUzs06gqqqK3r37sHp1TbFTMWt1LlbMzDqB6urqtFC5A+hT7HTa0CPAJcVOwtqZixUz\ns06lD9C32Em0IR8G2hR5gq2ZmZmVNBcrZmZmVtJcrJiZmVlJc7FiZmZmJc3FipmZmZU0FytmZmZW\n0v5/e/cfs1dZ33H8/QlVK4gahnYgP8rUwKL80qggokGDxAgqZtNFBkWSAoZkDJcMf4JiHAEU8Ufc\nxpSViiJoNoWpQ0pgMNCyoA5Yy6YOhiCt4g+0CIj0uz/Oedq7d5+nbQI959jzfiVP7vtc59z3/b2b\nnqefXtd1rmNYkSRJg2ZYkSRJg2ZYkSRJg2ZYkSRJg2ZYkSRJg2ZYkSRJg2ZYkSRJg2ZYkSRJg2ZY\nkSRJg2ZYmUWSU5LcmeShJN9O8pK+a5IkaawMK1OSvBX4KHAmcCDwn8BVSXbutTBJkkbKsLKx04C/\nr6qlVXUHcDLwG+CEfsuSJGmcDCsTkjwJeDFwzUxbVRWwDDi4r7okSRqzeX0XMDA7A9sBq6faVwN7\nz/Ga+QArV67cimX1b/33+zqwLX/XG5uH7wP391rI1vWL5mFb/3s7Jp6j25iRnKMT32/+po5L03Eg\ngCS7APcCB1fV8on2c4BXVtVGvStJ3gZ8vrsqJUna5hxTVV+Ya6c9Kxu6H3gMWDDVvgBYNcdrrgKO\nAV0sNswAAAkNSURBVO4CHt5qlUmStO2ZDyyk+bd0TvasTEnybWB5VZ3abge4G/hEVZ3Xa3GSJI2Q\nPSsbOx9YkuQW4Gaaq4O2B5b0WZQkSWNlWJlSVZe3a6qcRTP88z3giKr6ab+VSZI0Tg4DSZKkQXOd\nFUmSNGiGFY1Skh234JhXdVGLpI0leeUWHPPJLmpR/wwrGqsrkzxlrp1tUPmXDuuRtKErkhww1842\nqCzqsB71yLCisfoD4PIkG50D7f/ovoZXgEl9+gzwr0meN70jyceBtwNHdV6VeuEEW41Skl2BG4Ab\nq+q4ifZDaYLK56rqlL7qkwRJLgJeDby8qn7ctl0ALAaOrKpr+6xP3TGsaLSSPJcmsHypqk5N8grg\nG8Dnq+rkfquT1PZ8fhnYBzgUeC9wMnBUVV2zqddq22JY0agl2Q+4DrgCOBq4rKpO7LUoSeskeTJN\nb+f+wA7AG6tqWb9VqWuGFY1SkqdPbB4C/DPwFeAkYN1JUVW/6rg0SUCSv5jY3BF4P839YzboUamq\nT3RZl/phWNEoJVnLRCgB0j7WxHZV1XadFiYJgCR3bsFhVVV/tNWLUe9cbl9jdVjfBUiaW1Xt1XcN\nGg57ViRJ0qDZs6JRSzIPeAHwh23TKmBFVT3aX1WSYN3k2jcBB7PhOXoT8NWq+m1ftalb9qxolNpL\nIs8CTgGeMbX7AeBTwJlVtbbr2iRBuxjcVcCuwHJgdbtrAfAy4B7gdVX1g34qVJcMKxqlJOcCx7P+\nCoPJX4SvBT4ELKmq03spUBq5JFcDDwLHTV+V117NtxR4alUd0Ud96pZhRaOUZBWwqKqummP/EcDS\nqlrQbWWSAJL8BnhpVd0+x/59geVVtX23lakP3htIY7Uj8ONN7L+PZgEqSf34JbBwE/sXtsdoBAwr\nGqvrgI8k2Xl6R9t2TnuMpH58Blia5LQk+yVZ0P7sl+Q0mhuNXthvieqKw0AapSS7A1+nuefIbWw4\nZ2VfYAXNjdJ+1E+FkpKcDpxKcyXQ5IKNq4ALqurcvmpTtwwrGq32iqAjgIPY8LLIbwHf9EogaRiS\n7MXEOVpVW7K6rbYhhhVJkjRoLgontZI8iWbS3k+q6oGey5E0IckOwFuA59FMgL+0qn7Wb1XqihNs\nNUpJ/jrJU9vn2yX5CLAGuAO4P8lFbXiR1IMkK5Ls1D7fHfgv4GPA4TQLOq5oh4c0AoYVjdXZNJcv\nA5wGnACcRDO59njg9W27pH7sw/re/7OBe4E9q+qlwB7ArcCHe6pNHXMYSGOViedvA95VVUva7RVJ\nAN4NeLWB1L+DgZNnhmerak2SM4Ev9luWumLPisZsZnb5HjQ3Rpt0E2AXs9SvmXN0Ps08lUn3As/q\nthz1xZ4VjdniJGuA3wI7Te3bEXik+5IkTbgmye+ApwN7A5NL7+8JOMF2JAwrGqu7gcXt80eAFwHX\nT+w/DPjvrouStM4Hp7bXTG0fBdzQUS3qmeusSLNIchDwSFV9t+9aJGnsDCuSJGnQnGCr0UuyW7v0\n/gbPJQ1HkmcnOT/Jbn3Xou75S1lqblq4cJbnkobjWJqbGp7QdyHqnmFF2nDNlcx5lKQ+LQKuaR81\nMoYVSdKgJXkRzT2BjgN2SnJozyWpY4YVSdLQLQKurKpVwJdobomhETGsSJIGK8k8mltiLG2bLgH+\nZOZGpBoHw4okaciOBB4DvgFQVdfTrFz75j6LUrcMK5KkITsOuLSq1k60XYJDQaNiWJEkDVKSnYHX\ns34IaMYlwGGuuTIe3htIgr8Bfj7Lc0n9+jXw/Kq6e7Kxqv4nyV54I8PRcLl9SZI0aA4DSZKkQTOs\naLSS7Jrko0nmT7XPS3JekoX9VCZJmmRY0ZjdB7wFOHqq/XU0i1Dd03lFkqSNGFY0WtVM2LqE5tLI\nSccCX6yq33VflSRpmhNsNWpJ9gFuBXavqtVJnkHT4/KKqvpOv9VJksCeFY1cVd0B3AIc0zb9KfBD\ng4okDYdhRWoWnJoZCjoWuLjHWiRJUxwG0ugleSbN0M+fAZcDe1TV6n6rkiTNsGdFo1dVvwSuAD4L\nLDOoSNKwGFakxlJgJxwCkqTBcRhIApIE2AO4p6oe67seSdJ6hhVJkjRoDgNJkqRBM6xIkqRBM6xI\nkqRBM6xIkqRBM6xIkqRBM6xIkqRBM6xIkqRBM6xIkqRBM6xIkqRBM6xIkqRBM6xI6lWSa5Oc33cd\nkobLewNJ6lWSZwKPVtWDfdciaZgMK5IkadAcBpLUq8lhoCR3Jnl3ks8m+VWS/0uyeOr45yS5NMnP\nkqxJcnOSl0zsf0eSHyR5JMnKJH8+9fq1SU5McmWSB5OsSHJQkue2taxJcmOSvaZe98YktyR5qH3/\nM5L4O1TqgCeapKF5J/AfwAHAp4G/TfJ8gCQ7ANcDuwBHAvsCZ9P+LktyNHABcB7wAuBC4B+TvGrq\nM94HLAH2B1YCXwD+Dvgw8GIgwKdmDk5yKHAx8DFgH+AkYBHw3ifyi0uancNAknqV5Frgu1X1ziR3\nAv9WVcdP7F8FnFFVFyY5ETgX2LOqHpjlvf4duK2q3jHRdhmwfVUd1W6vBc6qqg+02y8DvgW8vaou\nbtveClxUVTu021cDy6rqnIn3PQY4t6qe8wT+cUiahT0rkobmtqntVcCz2+f70wSbjYJK64+Bm6ba\nbmzb5/qM1e3j7VNt85M8beJzz0jy65kf4B+ABUnmb/LbSHrc5vVdgCRNeXRqu1j/H6uHtsJn1Cba\nZj73acAZwD9Nv1FVPfwE1SRpDvasSPp9citwQHu582xWAodMtR0CrNjM+25uPPw7wN5V9b/TP5sv\nWdLjZc+KpN8nlwLvAb6S5D3AfcCBwL1VtZxmYu1lSb4HLAPeABwNvGYz75vNtJ0FXJnkR8CXgbU0\nQ0MvrKr3P47vI2kL2LMiqW/F+p6N2Xo41rVV1aPA4cBPgK/R9LScDjzW7v8qcCrwVzRzUBYDx1fV\nDbO935a2VdU3aa4+Ohy4mWZC7l8Cd23uy0l6/LwaSJIkDZo9K5IkadAMK5IkadAMK5IkadAMK5Ik\nadAMK5IkadAMK5IkadAMK5IkadAMK5IkadAMK5IkadAMK5IkadAMK5IkadD+H4YFictr34uMAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x99157f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGLCAYAAAAVjZKvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyYiIiIiUlJIRERERKSklIyIiIlJS/w+YhZ7X1JyjZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c72550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAIJCAYAAABkyn0VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsPZ/xbDnTLL+J5QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c72be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi0AAAJECAYAAADaEhTgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmJmZWVv4fyJQlyTnJFqOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d6ed30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot2grid((1,3), (0,0))\n",
    "train_data.sex.value_counts().plot(kind='bar')\n",
    "plt.title(u\"sex\")\n",
    "plt.ylabel(u\"numbers\")\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot2grid((1,3), (0,2))\n",
    "train_data.income.value_counts().plot(kind='bar')\n",
    "plt.title(u\"income\")\n",
    "plt.ylabel(u\"numbers\")\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "# income and sex\n",
    "sex_income_male = train_data.income[train_data.sex==\" Male\"].value_counts()\n",
    "sex_income_female = train_data.income[train_data.sex==\" Female\"].value_counts()\n",
    "df = pd.DataFrame({u'Male':sex_income_male, u'Female':sex_income_female})\n",
    "df.plot(kind='bar')\n",
    "plt.title(u\"income and sex distribution\")\n",
    "plt.xlabel(u\"income\")\n",
    "plt.ylabel(u\"numbers\")\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "# income and education_num\n",
    "edu_income_more = train_data.education_num[train_data.income==\" >50K\"].value_counts()\n",
    "edu_income_less = train_data.education_num[train_data.income==\" <=50K\"].value_counts()\n",
    "df2 = pd.DataFrame({u'>50K':edu_income_more, u'<=50K':edu_income_less})\n",
    "df2.plot(kind='bar')\n",
    "plt.title(\"Income and edu_num distribution\")\n",
    "plt.xlabel(\"edu_nums\")\n",
    "plt.ylabel(\"nubmers\")\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "# income and race\n",
    "race_income_more = train_data.race[train_data.income==\" >50K\"].value_counts()\n",
    "race_income_less = train_data.race[train_data.income==\" <=50K\"].value_counts()\n",
    "df = pd.DataFrame({u'>50K':race_income_more, u'<=50K':race_income_less})\n",
    "df.plot(kind='bar')\n",
    "plt.title(\"Income and race distribution\")\n",
    "plt.xlabel(\"race\")\n",
    "plt.ylabel(\"numbers\")\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "# income and native_country\n",
    "na_county_more = train_data.native_country[train_data.income == \" >50K\"].value_counts()\n",
    "na_county_less = train_data.native_country[train_data.income == \" <=50K\"].value_counts()\n",
    "df_na = pd.DataFrame({u'>50K':na_county_more, u'<=50K':na_county_less})\n",
    "df_na.plot(kind='bar')\n",
    "plt.title(\"Income and native country\")\n",
    "plt.xlabel(\"native country\")\n",
    "plt.ylabel(\"income\")\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi0AAAJECAYAAADaEhTgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmJmZWVv4fyJQlyTnJFqOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa861860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# income and native_country\n",
    "na_county_more = train_data.native_country[train_data.income == \" >50K\"].value_counts()\n",
    "na_county_less = train_data.native_country[train_data.income == \" <=50K\"].value_counts()\n",
    "df_na = pd.DataFrame({u'>50K':na_county_more, u'<=50K':na_county_less})\n",
    "df_na.plot(kind='bar')\n",
    "plt.title(\"Income and native country\")\n",
    "plt.xlabel(\"native country\")\n",
    "plt.ylabel(\"income\")\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAGHCAYAAACeWnkeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLClUhBBCCOFY/w+rrtfLsO7I/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9ae4710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh0AAAGLCAYAAABjtyAkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFEVRFEWpCf8Dkn+hH7Td/IgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x995e2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# age and income curve\n",
    "\n",
    "train_data.age[train_data.income==\" >50K\"].plot(kind='kde')\n",
    "train_data.age[train_data.income==\" <=50K\"].plot(kind='kde')\n",
    "plt.xlabel(\"age\")\n",
    "plt.ylabel(\"bar\")\n",
    "plt.xlim(16,91)\n",
    "plt.title(\"age and income distribution\")\n",
    "plt.legend((u'>50K', u'<=50K'), loc='best')\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "age_income_more = train_data.age[train_data.income==\" >50K\"].value_counts()\n",
    "age_income_less = train_data.age[train_data.income==\" <=50K\"].value_counts()\n",
    "df = DataFrame({u'>50K':age_income_more, u'<=50K':age_income_less})\n",
    "df.plot(kind='bar', color='rb')\n",
    "\n",
    "plt.xlabel(\"age\")\n",
    "plt.ylabel(\"numbers\")\n",
    "plt.title(\"age and income distribution\")\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAGHCAYAAABrpPKuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDAaDU/H/rasv5qLLnUwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9adb908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hourse_per_week and income\n",
    "fig.set(alpha=0.45)\n",
    "train_data.hours_per_week[train_data.income==\" >50K\"].plot(kind='kde')\n",
    "train_data.hours_per_week[train_data.income==\" <=50K\"].plot(kind='kde')\n",
    "plt.xlabel(\"hours per week\")\n",
    "plt.ylabel(\"income\")\n",
    "plt.xlim(0,70)\n",
    "\n",
    "plt.title(\"hours per week and income distribution\")\n",
    "plt.legend((u'>50K', u'<=50K'), loc='best')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               32561\n",
       "workclass         32561\n",
       "fnlwgt            32561\n",
       "education         32561\n",
       "education_num     32561\n",
       "marital_status    32561\n",
       "occupation        32561\n",
       "relationship      32561\n",
       "race              32561\n",
       "sex               32561\n",
       "capital_gain      32561\n",
       "capital_loss      32561\n",
       "hours_per_week    32561\n",
       "native_country    32561\n",
       "income            32561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#train_data.hours_per_week.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.xticks?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# fnlwgt and income\n",
    "train_data.fnlwgt[train_data.income==\" >50K\"].plot(kind='kde')\n",
    "train_data.fnlwgt[train_data.income==\" <=50K\"].plot(kind='kde')\n",
    "plt.xlabel(\"fnlwgt\")\n",
    "plt.ylabel(\"income\")\n",
    "plt.title(\"fnlwgt and income distribution\")\n",
    "plt.legend((u'>50K', u'<=50K'), loc='best')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# capital_gain and income\n",
    "train_data.capital_gain[train_data.income==\" >50K\"].plot(kind='kde')\n",
    "train_data.capital_gain[train_data.income==\" <=50K\"].plot(kind='kde')\n",
    "plt.xlabel(\"capital_gain\")\n",
    "plt.ylabel(\"income\")\n",
    "plt.title(\"capital_gain and income distribution\")\n",
    "plt.legend((u'>50K', u'<=50K'), loc='best')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# capital_loss and income\n",
    "train_data.capital_loss[train_data.income==\" >50K\"].plot(kind='kde')\n",
    "train_data.capital_loss[train_data.income==\" <=50K\"].plot(kind='kde')\n",
    "plt.xlabel(\"capital_loss\")\n",
    "plt.ylabel(\"income\")\n",
    "plt.title(\"capital_loss and income distribution\")\n",
    "plt.legend((u'>50K', u'<=50K'), loc='best')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 不同年龄段的男/女收入分布\n",
    "age_sex_income_more = train_data.age[train_data.sex==\" Male\"][train_data.income==\" >50K\"].value_counts()\n",
    "age_sex_income_less = train_data.age[train_data.sex==\" Male\"][train_data.income==\" <=50K\"].value_counts()\n",
    "\n",
    "df = DataFrame({u'>50K':age_sex_income_more, u'<=50K':age_sex_income_less})\n",
    "df.plot(kind='bar', stacked=True, color='rb')\n",
    "\n",
    "plt.xlabel(\"age\")\n",
    "plt.ylabel(\"numbers\")\n",
    "plt.title(\"age and income distribution of male\")\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "# 不同年龄段的男/女收入分布\n",
    "age_sex_income_more = train_data.age[train_data.sex==\" Female\"][train_data.income==\" >50K\"].value_counts()\n",
    "age_sex_income_less = train_data.age[train_data.sex==\" Female\"][train_data.income==\" <=50K\"].value_counts()\n",
    "\n",
    "df = DataFrame({u'>50K':age_sex_income_more, u'<=50K':age_sex_income_less})\n",
    "df.plot(kind='bar', stacked=True, color='rb')\n",
    "\n",
    "plt.xlabel(\"age\")\n",
    "plt.ylabel(\"numbers\")\n",
    "plt.title(\"age and time distribution of female\")\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 家庭中的角色与收入的分布关系\n",
    "fig.set(alpha=0.45)\n",
    "fig.set_size_inches(20, 10.5)\n",
    "rel_income_more = train_data.relationship[train_data.income==\" >50K\"].value_counts()\n",
    "rel_income_less = train_data.relationship[train_data.income==\" <=50K\"].value_counts()\n",
    "\n",
    "df = pd.DataFrame({u'>50K': rel_income_more, u'<=50K': rel_income_less})\n",
    "df.plot(kind='barh')\n",
    "plt.title(\"Relationship and Income\")\n",
    "plt.xlabel(\"relationship\")\n",
    "plt.ylabel(\"numbers\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAowAAAGHCAYAAAA6HAOIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x67f66a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# workclass and income\n",
    "work_income_more = train_data.workclass[train_data.income==\" >50K\"].value_counts()\n",
    "work_income_less = train_data.workclass[train_data.income==\" <=50K\"].value_counts()\n",
    "df = DataFrame({u'>50K':work_income_more, u'<=50K':work_income_less})\n",
    "df.plot(kind='barh', stacked=True)\n",
    "plt.title(\"Workclass and income distribution\")\n",
    "plt.xlabel(\"workclass\")\n",
    "plt.ylabel(\"numbers\")\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " White                 7117\n",
       " Black                  387\n",
       " Asian-Pac-Islander     276\n",
       " Amer-Indian-Eskimo      36\n",
       " Other                   25\n",
       "Name: race, dtype: int64"
      ]
     },
     "execution_count": 567,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.race[train_data.income==\" >50K\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " White                 20699\n",
       " Black                  2737\n",
       " Asian-Pac-Islander      763\n",
       " Amer-Indian-Eskimo      275\n",
       " Other                   246\n",
       "Name: race, dtype: int64"
      ]
     },
     "execution_count": 568,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.race[train_data.income==\" <=50K\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "male:  21790\n",
      "male_more: 6662, male_less: 15128\n",
      "female:  10771\n",
      "female_more: 1179, female_less: 9592\n"
     ]
    }
   ],
   "source": [
    "data_train_gender[:10]\n",
    "male_income = [x for x in data_train_gender if x[0]==\"b' Male'\"]\n",
    "female_income = [x for x in data_train_gender if x[0]==\"b' Female'\"]\n",
    "\n",
    "male_more = len([x for x in male_income if x[1]==\"b' >50K'\"])\n",
    "male_less = len([x for x in male_income if x[1]==\"b' <=50K'\"])\n",
    "female_more = len([x for x in female_income if x[1]==\"b' >50K'\"])\n",
    "female_less = len([x for x in female_income if x[1]==\"b' <=50K'\"])\n",
    "\n",
    "print(\"male: \", len(male_income))\n",
    "print((\"male_more: {0}, male_less: {1}\").format(male_more, male_less))\n",
    "\n",
    "print(\"female: \", len(female_income))\n",
    "print((\"female_more: {0}, female_less: {1}\").format(female_more, female_less))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### hours per week 特征进行离散化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hours_per_week_col = train_data.hours_per_week.map(lambda x: 1 if x==40 else 0)\n",
    "#hours_per_week_col.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 提取训练数据的 continous 属性的特征， 及 label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train set length:  32561\n",
      "train set label length:  32561\n"
     ]
    }
   ],
   "source": [
    "# age\tworkclass\tfnlwgt\teducation\teducation_num\tmarital_status\toccupation\trelationship\trace\tsex\tcapital_gain\tcapital_loss\thours_per_week\tnative_country\tincome\n",
    "\n",
    "hours_per_week_col = train_data.hours_per_week.map(lambda x: 1 if x==40 else 0)\n",
    "# 提取样本的所有 continuous 类型的特征\n",
    "train_set = DataFrame(train_data, columns=['age', 'workclass', 'education_num', 'marital_status', 'relationship', 'race', 'sex', 'hours_per_week'])\n",
    "print (\"train set length: \", len(train_set))\n",
    "\n",
    "train_set_label = pd.Series(train_data.income.map(lambda x: 1 if x==' >50K' else 0))\n",
    "#print(type(data_for_train_label))\n",
    "\n",
    "print(\"train set label length: \", len(train_set_label))\n",
    "train_set.head(n=10)\n",
    "\n",
    "\n",
    "# 添加 sex 特征\n",
    "new_train_set = train_set.copy()\n",
    "new_train_col = train_data.sex.replace({\" Male\":1, \" Female\":0})\n",
    "new_train_set['sex'] = pd.Series(new_train_col)\n",
    "\n",
    "\n",
    "\n",
    "# 添加离散化后的 hours_per_week 特征\n",
    "new_train_set['hours_per_week'] = pd.Series(hours_per_week_col)\n",
    "\n",
    "\n",
    "\n",
    "new_train_set['income'] = train_set_label\n",
    "new_train_set.head(5)\n",
    "new_train_set.to_csv(\"feature/xgb_train_data.csv\", index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 提取测试数据的 continous 属性的特征， 及 label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train set length:  16281\n",
      "train set label length:  16281\n"
     ]
    }
   ],
   "source": [
    "# age\tworkclass\tfnlwgt\teducation\teducation_num\tmarital_status\toccupation\trelationship\trace\tsex\tcapital_gain\tcapital_loss\thours_per_week\tnative_country\tincome\n",
    "\n",
    "test_hours_per_week_col = test_data.hours_per_week.map(lambda x: 1 if x==40 else 0)\n",
    "# 提取样本的所有 continuous 类型的特征\n",
    "test_set = DataFrame(test_data, columns=['age', 'workclass', 'education_num', 'marital_status', 'relationship', 'race', 'sex', 'hours_per_week'])\n",
    "print (\"train set length: \", len(test_set))\n",
    "\n",
    "test_set_label = pd.Series(test_data.income.map(lambda x: 1 if x==' >50K' else 0))\n",
    "#print(type(data_for_train_label))\n",
    "\n",
    "print(\"train set label length: \", len(test_set_label))\n",
    "test_set.head(n=10)\n",
    "\n",
    "\n",
    "# 添加 sex 特征\n",
    "new_test_set = test_set.copy()\n",
    "new_test_col = test_data.sex.replace({\" Male\":1, \" Female\":0})\n",
    "new_test_set['sex'] = pd.Series(new_test_col)\n",
    "\n",
    "\n",
    "\n",
    "# 添加离散化后的 hours_per_week 特征\n",
    "new_test_set['hours_per_week'] = pd.Series(test_hours_per_week_col)\n",
    "\n",
    "\n",
    "\n",
    "new_test_set['income'] = test_set_label\n",
    "new_test_set.head(5)\n",
    "new_test_set.to_csv(\"feature/xgb_test_data.csv\", index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_train_set.to_csv?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           age  workclass  education_num  marital_...\n",
       "1           income\n",
       "0       <=50K\n",
       "1       <=50K\n",
       "2   ...\n",
       "dtype: object"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对 训练数据的 label 进行处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "label_dic = {\" >50K\":1, \" <=50K\":0}\n",
    "sex_dic = {\" Male\":1, \" Femal\":0}\n",
    "train_label = [label_dic[x[0]] for x in data_for_train_label.values]\n",
    "#print(len(train_label))\n",
    "#print(train_label[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 提取测试数据的 contious 特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test set length:  16281\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>education_num</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  education_num  hours_per_week  sex\n",
       "0   25              7               1    1\n",
       "1   38              9               0    1\n",
       "2   28             12               1    1\n",
       "3   44             10               1    1\n",
       "4   18             10               0    0"
      ]
     },
     "execution_count": 590,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set = DataFrame(test_data, columns=['age', 'education_num', 'hours_per_week'])\n",
    "print (\"test set length: \", len(test_set))\n",
    "\n",
    "\n",
    "# 添加 sex 特征\n",
    "new_test_set = test_set.copy()\n",
    "new_test_col = test_data.sex.replace({\" Male\":1, \" Female\":0})\n",
    "new_test_set['sex'] = pd.Series(new_test_col)\n",
    "new_test_set.head(5)\n",
    "\n",
    "\n",
    "#test_hours_per_week_col = test_data.hours_per_week.map(lambda x: 1 if x==40 else 0)\n",
    "#test_hours_per_week_col.head(10)\n",
    "\n",
    "\n",
    "# 添加离散化后的 hours_per_week 特征\n",
    "new_test_set['hours_per_week'] = pd.Series(test_hours_per_week_col)\n",
    "\n",
    "new_test_set.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " <=50K    0.763774\n",
       " >50K     0.236226\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 596,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label.income.value_counts()/test_label.income.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 特征缩放\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "min_max = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   workclass  fnlwgt      education  education_num       marital_status  \\\n",
       "0   25     Private  226802           11th              7        Never-married   \n",
       "1   38     Private   89814        HS-grad              9   Married-civ-spouse   \n",
       "2   28   Local-gov  336951     Assoc-acdm             12   Married-civ-spouse   \n",
       "3   44     Private  160323   Some-college             10   Married-civ-spouse   \n",
       "4   18           ?  103497   Some-college             10        Never-married   \n",
       "\n",
       "           occupation relationship    race      sex  capital_gain  \\\n",
       "0   Machine-op-inspct    Own-child   Black     Male             0   \n",
       "1     Farming-fishing      Husband   White     Male             0   \n",
       "2     Protective-serv      Husband   White     Male             0   \n",
       "3   Machine-op-inspct      Husband   Black     Male          7688   \n",
       "4                   ?    Own-child   White   Female             0   \n",
       "\n",
       "   capital_loss  hours_per_week  native_country  income  \n",
       "0             0              40   United-States   <=50K  \n",
       "1             0              50   United-States   <=50K  \n",
       "2             0              40   United-States    >50K  \n",
       "3             0              40   United-States    >50K  \n",
       "4             0              30   United-States   <=50K  "
      ]
     },
     "execution_count": 599,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对测试数据的 label 进行处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 1, 1, 0, 0, 0, 1, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "test_label = DataFrame(test_data, columns=['income'])\n",
    "#print (\"length of test_label: \", len(test_label))\n",
    "\n",
    "test_set_label = [label_dic[x[0]] for x in test_label.values]\n",
    "print(test_set_label[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf = LogisticRegressionCV(cv=10)\n",
    "clf_svm = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=10, class_weight=None, cv=10, dual=False,\n",
       "           fit_intercept=True, intercept_scaling=1.0, max_iter=100,\n",
       "           multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
       "           refit=True, scoring=None, solver='lbfgs', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(new_train_set, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 571,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_svm.fit(new_train_set, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svc_prediction = clf_svm.predict(new_test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LR预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score as acc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prediction = clf.predict(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "acc_score?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  0.799090964928\n",
      "error:  0.200909035072\n"
     ]
    }
   ],
   "source": [
    "score = acc_score(test_set_label, prediction)\n",
    "print(\"score: \", score)\n",
    "error = 1-score\n",
    "print (\"error: \", error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVC预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.771819912782\n",
      "error:  0.228180087218\n"
     ]
    }
   ],
   "source": [
    "svc_score = acc_score(test_set_label, svc_prediction)\n",
    "print(\"accuracy: \", svc_score)\n",
    "error = 1-svc_score\n",
    "print(\"error: \", error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 所有 continuous 类型特征加入训练\n",
    "score:  0.799275228794\n",
    "error:  0.200724771206"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.04240404  0.36540655  1.28310195 -0.14688063]]\n"
     ]
    }
   ],
   "source": [
    "weights = clf.coef_\n",
    "print(weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对增加了 sex 特征后的数据进行训练\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=10, class_weight=None, cv=10, dual=False,\n",
       "           fit_intercept=True, intercept_scaling=1.0, max_iter=100,\n",
       "           multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
       "           refit=True, scoring=None, solver='lbfgs', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(new_train_set, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  0.768441741908\n",
      "error:  0.231558258092\n"
     ]
    }
   ],
   "source": [
    "score = acc_score(test_set_label, clf.predict(new_test_set))\n",
    "print(\"score: \", score)\n",
    "error = 1-score\n",
    "print (\"error: \", error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "score:  0.799398071372\n",
    "error:  0.200601928628\n",
    "[[ 0.04548398  0.35339825  0.03570044  1.12167643]]\n",
    "    \n",
    "score:  0.798231066888\n",
    "error:  0.201768933112"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "score = clf.score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_for_train.head?"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
